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Machine Learning: Algorithms, Real-World Applications and Research Directions

• Review Article
• Published: 22 March 2021
• Volume 2 , article number  160 , ( 2021 )

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• Iqbal H. Sarker   ORCID: orcid.org/0000-0003-1740-5517 1 , 2  

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In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated  applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning , which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

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Introduction

We live in the age of data, where everything around us is connected to a data source, and everything in our lives is digitally recorded [ 21 , 103 ]. For instance, the current electronic world has a wealth of various kinds of data, such as the Internet of Things (IoT) data, cybersecurity data, smart city data, business data, smartphone data, social media data, health data, COVID-19 data, and many more. The data can be structured, semi-structured, or unstructured, discussed briefly in Sect. “ Types of Real-World Data and Machine Learning Techniques ”, which is increasing day-by-day. Extracting insights from these data can be used to build various intelligent applications in the relevant domains. For instance, to build a data-driven automated and intelligent cybersecurity system, the relevant cybersecurity data can be used [ 105 ]; to build personalized context-aware smart mobile applications, the relevant mobile data can be used [ 103 ], and so on. Thus, the data management tools and techniques having the capability of extracting insights or useful knowledge from the data in a timely and intelligent way is urgently needed, on which the real-world applications are based.

The worldwide popularity score of various types of ML algorithms (supervised, unsupervised, semi-supervised, and reinforcement) in a range of 0 (min) to 100 (max) over time where x-axis represents the timestamp information and y-axis represents the corresponding score

Artificial intelligence (AI), particularly, machine learning (ML) have grown rapidly in recent years in the context of data analysis and computing that typically allows the applications to function in an intelligent manner [ 95 ]. ML usually provides systems with the ability to learn and enhance from experience automatically without being specifically programmed and is generally referred to as the most popular latest technologies in the fourth industrial revolution (4 IR or Industry 4.0) [ 103 , 105 ]. “Industry 4.0” [ 114 ] is typically the ongoing automation of conventional manufacturing and industrial practices, including exploratory data processing, using new smart technologies such as machine learning automation. Thus, to intelligently analyze these data and to develop the corresponding real-world applications, machine learning algorithms is the key. The learning algorithms can be categorized into four major types, such as supervised, unsupervised, semi-supervised, and reinforcement learning in the area [ 75 ], discussed briefly in Sect. “ Types of Real-World Data and Machine Learning Techniques ”. The popularity of these approaches to learning is increasing day-by-day, which is shown in Fig. 1 , based on data collected from Google Trends [ 4 ] over the last five years. The x - axis of the figure indicates the specific dates and the corresponding popularity score within the range of \(0 \; (minimum)\) to \(100 \; (maximum)\) has been shown in y - axis . According to Fig. 1 , the popularity indication values for these learning types are low in 2015 and are increasing day by day. These statistics motivate us to study on machine learning in this paper, which can play an important role in the real-world through Industry 4.0 automation.

In general, the effectiveness and the efficiency of a machine learning solution depend on the nature and characteristics of data and the performance of the learning algorithms . In the area of machine learning algorithms, classification analysis, regression, data clustering, feature engineering and dimensionality reduction, association rule learning, or reinforcement learning techniques exist to effectively build data-driven systems [ 41 , 125 ]. Besides, deep learning originated from the artificial neural network that can be used to intelligently analyze data, which is known as part of a wider family of machine learning approaches [ 96 ]. Thus, selecting a proper learning algorithm that is suitable for the target application in a particular domain is challenging. The reason is that the purpose of different learning algorithms is different, even the outcome of different learning algorithms in a similar category may vary depending on the data characteristics [ 106 ]. Thus, it is important to understand the principles of various machine learning algorithms and their applicability to apply in various real-world application areas, such as IoT systems, cybersecurity services, business and recommendation systems, smart cities, healthcare and COVID-19, context-aware systems, sustainable agriculture, and many more that are explained briefly in Sect. “ Applications of Machine Learning ”.

Based on the importance and potentiality of “Machine Learning” to analyze the data mentioned above, in this paper, we provide a comprehensive view on various types of machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, the key contribution of this study is explaining the principles and potentiality of different machine learning techniques, and their applicability in various real-world application areas mentioned earlier. The purpose of this paper is, therefore, to provide a basic guide for those academia and industry people who want to study, research, and develop data-driven automated and intelligent systems in the relevant areas based on machine learning techniques.

The key contributions of this paper are listed as follows:

To define the scope of our study by taking into account the nature and characteristics of various types of real-world data and the capabilities of various learning techniques.

To provide a comprehensive view on machine learning algorithms that can be applied to enhance the intelligence and capabilities of a data-driven application.

To discuss the applicability of machine learning-based solutions in various real-world application domains.

To highlight and summarize the potential research directions within the scope of our study for intelligent data analysis and services.

The rest of the paper is organized as follows. The next section presents the types of data and machine learning algorithms in a broader sense and defines the scope of our study. We briefly discuss and explain different machine learning algorithms in the subsequent section followed by which various real-world application areas based on machine learning algorithms are discussed and summarized. In the penultimate section, we highlight several research issues and potential future directions, and the final section concludes this paper.

Types of Real-World Data and Machine Learning Techniques

Machine learning algorithms typically consume and process data to learn the related patterns about individuals, business processes, transactions, events, and so on. In the following, we discuss various types of real-world data as well as categories of machine learning algorithms.

Types of Real-World Data

Usually, the availability of data is considered as the key to construct a machine learning model or data-driven real-world systems [ 103 , 105 ]. Data can be of various forms, such as structured, semi-structured, or unstructured [ 41 , 72 ]. Besides, the “metadata” is another type that typically represents data about the data. In the following, we briefly discuss these types of data.

Structured: It has a well-defined structure, conforms to a data model following a standard order, which is highly organized and easily accessed, and used by an entity or a computer program. In well-defined schemes, such as relational databases, structured data are typically stored, i.e., in a tabular format. For instance, names, dates, addresses, credit card numbers, stock information, geolocation, etc. are examples of structured data.

Unstructured: On the other hand, there is no pre-defined format or organization for unstructured data, making it much more difficult to capture, process, and analyze, mostly containing text and multimedia material. For example, sensor data, emails, blog entries, wikis, and word processing documents, PDF files, audio files, videos, images, presentations, web pages, and many other types of business documents can be considered as unstructured data.

Semi-structured: Semi-structured data are not stored in a relational database like the structured data mentioned above, but it does have certain organizational properties that make it easier to analyze. HTML, XML, JSON documents, NoSQL databases, etc., are some examples of semi-structured data.

Metadata: It is not the normal form of data, but “data about data”. The primary difference between “data” and “metadata” is that data are simply the material that can classify, measure, or even document something relative to an organization’s data properties. On the other hand, metadata describes the relevant data information, giving it more significance for data users. A basic example of a document’s metadata might be the author, file size, date generated by the document, keywords to define the document, etc.

In the area of machine learning and data science, researchers use various widely used datasets for different purposes. These are, for example, cybersecurity datasets such as NSL-KDD [ 119 ], UNSW-NB15 [ 76 ], ISCX’12 [ 1 ], CIC-DDoS2019 [ 2 ], Bot-IoT [ 59 ], etc., smartphone datasets such as phone call logs [ 84 , 101 ], SMS Log [ 29 ], mobile application usages logs [ 137 ] [ 117 ], mobile phone notification logs [ 73 ] etc., IoT data [ 16 , 57 , 62 ], agriculture and e-commerce data [ 120 , 138 ], health data such as heart disease [ 92 ], diabetes mellitus [ 83 , 134 ], COVID-19 [ 43 , 74 ], etc., and many more in various application domains. The data can be in different types discussed above, which may vary from application to application in the real world. To analyze such data in a particular problem domain, and to extract the insights or useful knowledge from the data for building the real-world intelligent applications, different types of machine learning techniques can be used according to their learning capabilities, which is discussed in the following.

Types of Machine Learning Techniques

Machine Learning algorithms are mainly divided into four categories: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning [ 75 ], as shown in Fig. 2 . In the following, we briefly discuss each type of learning technique with the scope of their applicability to solve real-world problems.

Various types of machine learning techniques

Supervised: Supervised learning is typically the task of machine learning to learn a function that maps an input to an output based on sample input-output pairs [ 41 ]. It uses labeled training data and a collection of training examples to infer a function. Supervised learning is carried out when certain goals are identified to be accomplished from a certain set of inputs [ 105 ], i.e., a task-driven approach . The most common supervised tasks are “classification” that separates the data, and “regression” that fits the data. For instance, predicting the class label or sentiment of a piece of text, like a tweet or a product review, i.e., text classification, is an example of supervised learning.

Unsupervised: Unsupervised learning analyzes unlabeled datasets without the need for human interference, i.e., a data-driven process [ 41 ]. This is widely used for extracting generative features, identifying meaningful trends and structures, groupings in results, and exploratory purposes. The most common unsupervised learning tasks are clustering, density estimation, feature learning, dimensionality reduction, finding association rules, anomaly detection, etc.

Semi-supervised: Semi-supervised learning can be defined as a hybridization of the above-mentioned supervised and unsupervised methods, as it operates on both labeled and unlabeled data [ 41 , 105 ]. Thus, it falls between learning “without supervision” and learning “with supervision”. In the real world, labeled data could be rare in several contexts, and unlabeled data are numerous, where semi-supervised learning is useful [ 75 ]. The ultimate goal of a semi-supervised learning model is to provide a better outcome for prediction than that produced using the labeled data alone from the model. Some application areas where semi-supervised learning is used include machine translation, fraud detection, labeling data and text classification.

Reinforcement: Reinforcement learning is a type of machine learning algorithm that enables software agents and machines to automatically evaluate the optimal behavior in a particular context or environment to improve its efficiency [ 52 ], i.e., an environment-driven approach . This type of learning is based on reward or penalty, and its ultimate goal is to use insights obtained from environmental activists to take action to increase the reward or minimize the risk [ 75 ]. It is a powerful tool for training AI models that can help increase automation or optimize the operational efficiency of sophisticated systems such as robotics, autonomous driving tasks, manufacturing and supply chain logistics, however, not preferable to use it for solving the basic or straightforward problems.

Thus, to build effective models in various application areas different types of machine learning techniques can play a significant role according to their learning capabilities, depending on the nature of the data discussed earlier, and the target outcome. In Table 1 , we summarize various types of machine learning techniques with examples. In the following, we provide a comprehensive view of machine learning algorithms that can be applied to enhance the intelligence and capabilities of a data-driven application.

Machine Learning Tasks and Algorithms

In this section, we discuss various machine learning algorithms that include classification analysis, regression analysis, data clustering, association rule learning, feature engineering for dimensionality reduction, as well as deep learning methods. A general structure of a machine learning-based predictive model has been shown in Fig. 3 , where the model is trained from historical data in phase 1 and the outcome is generated in phase 2 for the new test data.

A general structure of a machine learning based predictive model considering both the training and testing phase

Classification Analysis

Classification is regarded as a supervised learning method in machine learning, referring to a problem of predictive modeling as well, where a class label is predicted for a given example [ 41 ]. Mathematically, it maps a function ( f ) from input variables ( X ) to output variables ( Y ) as target, label or categories. To predict the class of given data points, it can be carried out on structured or unstructured data. For example, spam detection such as “spam” and “not spam” in email service providers can be a classification problem. In the following, we summarize the common classification problems.

Binary classification: It refers to the classification tasks having two class labels such as “true and false” or “yes and no” [ 41 ]. In such binary classification tasks, one class could be the normal state, while the abnormal state could be another class. For instance, “cancer not detected” is the normal state of a task that involves a medical test, and “cancer detected” could be considered as the abnormal state. Similarly, “spam” and “not spam” in the above example of email service providers are considered as binary classification.

Multiclass classification: Traditionally, this refers to those classification tasks having more than two class labels [ 41 ]. The multiclass classification does not have the principle of normal and abnormal outcomes, unlike binary classification tasks. Instead, within a range of specified classes, examples are classified as belonging to one. For example, it can be a multiclass classification task to classify various types of network attacks in the NSL-KDD [ 119 ] dataset, where the attack categories are classified into four class labels, such as DoS (Denial of Service Attack), U2R (User to Root Attack), R2L (Root to Local Attack), and Probing Attack.

Multi-label classification: In machine learning, multi-label classification is an important consideration where an example is associated with several classes or labels. Thus, it is a generalization of multiclass classification, where the classes involved in the problem are hierarchically structured, and each example may simultaneously belong to more than one class in each hierarchical level, e.g., multi-level text classification. For instance, Google news can be presented under the categories of a “city name”, “technology”, or “latest news”, etc. Multi-label classification includes advanced machine learning algorithms that support predicting various mutually non-exclusive classes or labels, unlike traditional classification tasks where class labels are mutually exclusive [ 82 ].

Many classification algorithms have been proposed in the machine learning and data science literature [ 41 , 125 ]. In the following, we summarize the most common and popular methods that are used widely in various application areas.

Naive Bayes (NB): The naive Bayes algorithm is based on the Bayes’ theorem with the assumption of independence between each pair of features [ 51 ]. It works well and can be used for both binary and multi-class categories in many real-world situations, such as document or text classification, spam filtering, etc. To effectively classify the noisy instances in the data and to construct a robust prediction model, the NB classifier can be used [ 94 ]. The key benefit is that, compared to more sophisticated approaches, it needs a small amount of training data to estimate the necessary parameters and quickly [ 82 ]. However, its performance may affect due to its strong assumptions on features independence. Gaussian, Multinomial, Complement, Bernoulli, and Categorical are the common variants of NB classifier [ 82 ].

Linear Discriminant Analysis (LDA): Linear Discriminant Analysis (LDA) is a linear decision boundary classifier created by fitting class conditional densities to data and applying Bayes’ rule [ 51 , 82 ]. This method is also known as a generalization of Fisher’s linear discriminant, which projects a given dataset into a lower-dimensional space, i.e., a reduction of dimensionality that minimizes the complexity of the model or reduces the resulting model’s computational costs. The standard LDA model usually suits each class with a Gaussian density, assuming that all classes share the same covariance matrix [ 82 ]. LDA is closely related to ANOVA (analysis of variance) and regression analysis, which seek to express one dependent variable as a linear combination of other features or measurements.

Logistic regression (LR): Another common probabilistic based statistical model used to solve classification issues in machine learning is Logistic Regression (LR) [ 64 ]. Logistic regression typically uses a logistic function to estimate the probabilities, which is also referred to as the mathematically defined sigmoid function in Eq. 1 . It can overfit high-dimensional datasets and works well when the dataset can be separated linearly. The regularization (L1 and L2) techniques [ 82 ] can be used to avoid over-fitting in such scenarios. The assumption of linearity between the dependent and independent variables is considered as a major drawback of Logistic Regression. It can be used for both classification and regression problems, but it is more commonly used for classification.

K-nearest neighbors (KNN): K-Nearest Neighbors (KNN) [ 9 ] is an “instance-based learning” or non-generalizing learning, also known as a “lazy learning” algorithm. It does not focus on constructing a general internal model; instead, it stores all instances corresponding to training data in n -dimensional space. KNN uses data and classifies new data points based on similarity measures (e.g., Euclidean distance function) [ 82 ]. Classification is computed from a simple majority vote of the k nearest neighbors of each point. It is quite robust to noisy training data, and accuracy depends on the data quality. The biggest issue with KNN is to choose the optimal number of neighbors to be considered. KNN can be used both for classification as well as regression.

Support vector machine (SVM): In machine learning, another common technique that can be used for classification, regression, or other tasks is a support vector machine (SVM) [ 56 ]. In high- or infinite-dimensional space, a support vector machine constructs a hyper-plane or set of hyper-planes. Intuitively, the hyper-plane, which has the greatest distance from the nearest training data points in any class, achieves a strong separation since, in general, the greater the margin, the lower the classifier’s generalization error. It is effective in high-dimensional spaces and can behave differently based on different mathematical functions known as the kernel. Linear, polynomial, radial basis function (RBF), sigmoid, etc., are the popular kernel functions used in SVM classifier [ 82 ]. However, when the data set contains more noise, such as overlapping target classes, SVM does not perform well.

Decision tree (DT): Decision tree (DT) [ 88 ] is a well-known non-parametric supervised learning method. DT learning methods are used for both the classification and regression tasks [ 82 ]. ID3 [ 87 ], C4.5 [ 88 ], and CART [ 20 ] are well known for DT algorithms. Moreover, recently proposed BehavDT [ 100 ], and IntrudTree [ 97 ] by Sarker et al. are effective in the relevant application domains, such as user behavior analytics and cybersecurity analytics, respectively. By sorting down the tree from the root to some leaf nodes, as shown in Fig. 4 , DT classifies the instances. Instances are classified by checking the attribute defined by that node, starting at the root node of the tree, and then moving down the tree branch corresponding to the attribute value. For splitting, the most popular criteria are “gini” for the Gini impurity and “entropy” for the information gain that can be expressed mathematically as [ 82 ].

An example of a decision tree structure

An example of a random forest structure considering multiple decision trees

Random forest (RF): A random forest classifier [ 19 ] is well known as an ensemble classification technique that is used in the field of machine learning and data science in various application areas. This method uses “parallel ensembling” which fits several decision tree classifiers in parallel, as shown in Fig. 5 , on different data set sub-samples and uses majority voting or averages for the outcome or final result. It thus minimizes the over-fitting problem and increases the prediction accuracy and control [ 82 ]. Therefore, the RF learning model with multiple decision trees is typically more accurate than a single decision tree based model [ 106 ]. To build a series of decision trees with controlled variation, it combines bootstrap aggregation (bagging) [ 18 ] and random feature selection [ 11 ]. It is adaptable to both classification and regression problems and fits well for both categorical and continuous values.

Adaptive Boosting (AdaBoost): Adaptive Boosting (AdaBoost) is an ensemble learning process that employs an iterative approach to improve poor classifiers by learning from their errors. This is developed by Yoav Freund et al. [ 35 ] and also known as “meta-learning”. Unlike the random forest that uses parallel ensembling, Adaboost uses “sequential ensembling”. It creates a powerful classifier by combining many poorly performing classifiers to obtain a good classifier of high accuracy. In that sense, AdaBoost is called an adaptive classifier by significantly improving the efficiency of the classifier, but in some instances, it can trigger overfits. AdaBoost is best used to boost the performance of decision trees, base estimator [ 82 ], on binary classification problems, however, is sensitive to noisy data and outliers.

Extreme gradient boosting (XGBoost): Gradient Boosting, like Random Forests [ 19 ] above, is an ensemble learning algorithm that generates a final model based on a series of individual models, typically decision trees. The gradient is used to minimize the loss function, similar to how neural networks [ 41 ] use gradient descent to optimize weights. Extreme Gradient Boosting (XGBoost) is a form of gradient boosting that takes more detailed approximations into account when determining the best model [ 82 ]. It computes second-order gradients of the loss function to minimize loss and advanced regularization (L1 and L2) [ 82 ], which reduces over-fitting, and improves model generalization and performance. XGBoost is fast to interpret and can handle large-sized datasets well.

Stochastic gradient descent (SGD): Stochastic gradient descent (SGD) [ 41 ] is an iterative method for optimizing an objective function with appropriate smoothness properties, where the word ‘stochastic’ refers to random probability. This reduces the computational burden, particularly in high-dimensional optimization problems, allowing for faster iterations in exchange for a lower convergence rate. A gradient is the slope of a function that calculates a variable’s degree of change in response to another variable’s changes. Mathematically, the Gradient Descent is a convex function whose output is a partial derivative of a set of its input parameters. Let, \(\alpha\) is the learning rate, and \(J_i\) is the training example cost of \(i \mathrm{th}\) , then Eq. ( 4 ) represents the stochastic gradient descent weight update method at the \(j^\mathrm{th}\) iteration. In large-scale and sparse machine learning, SGD has been successfully applied to problems often encountered in text classification and natural language processing [ 82 ]. However, SGD is sensitive to feature scaling and needs a range of hyperparameters, such as the regularization parameter and the number of iterations.

Rule-based classification : The term rule-based classification can be used to refer to any classification scheme that makes use of IF-THEN rules for class prediction. Several classification algorithms such as Zero-R [ 125 ], One-R [ 47 ], decision trees [ 87 , 88 ], DTNB [ 110 ], Ripple Down Rule learner (RIDOR) [ 125 ], Repeated Incremental Pruning to Produce Error Reduction (RIPPER) [ 126 ] exist with the ability of rule generation. The decision tree is one of the most common rule-based classification algorithms among these techniques because it has several advantages, such as being easier to interpret; the ability to handle high-dimensional data; simplicity and speed; good accuracy; and the capability to produce rules for human clear and understandable classification [ 127 ] [ 128 ]. The decision tree-based rules also provide significant accuracy in a prediction model for unseen test cases [ 106 ]. Since the rules are easily interpretable, these rule-based classifiers are often used to produce descriptive models that can describe a system including the entities and their relationships.

Classification vs. regression. In classification the dotted line represents a linear boundary that separates the two classes; in regression, the dotted line models the linear relationship between the two variables

Regression Analysis

Regression analysis includes several methods of machine learning that allow to predict a continuous ( y ) result variable based on the value of one or more ( x ) predictor variables [ 41 ]. The most significant distinction between classification and regression is that classification predicts distinct class labels, while regression facilitates the prediction of a continuous quantity. Figure 6 shows an example of how classification is different with regression models. Some overlaps are often found between the two types of machine learning algorithms. Regression models are now widely used in a variety of fields, including financial forecasting or prediction, cost estimation, trend analysis, marketing, time series estimation, drug response modeling, and many more. Some of the familiar types of regression algorithms are linear, polynomial, lasso and ridge regression, etc., which are explained briefly in the following.

Simple and multiple linear regression: This is one of the most popular ML modeling techniques as well as a well-known regression technique. In this technique, the dependent variable is continuous, the independent variable(s) can be continuous or discrete, and the form of the regression line is linear. Linear regression creates a relationship between the dependent variable ( Y ) and one or more independent variables ( X ) (also known as regression line) using the best fit straight line [ 41 ]. It is defined by the following equations:

where a is the intercept, b is the slope of the line, and e is the error term. This equation can be used to predict the value of the target variable based on the given predictor variable(s). Multiple linear regression is an extension of simple linear regression that allows two or more predictor variables to model a response variable, y, as a linear function [ 41 ] defined in Eq. 6 , whereas simple linear regression has only 1 independent variable, defined in Eq. 5 .

Polynomial regression: Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is not linear, but is the polynomial degree of \(n^\mathrm{th}\) in x [ 82 ]. The equation for polynomial regression is also derived from linear regression (polynomial regression of degree 1) equation, which is defined as below:

Here, y is the predicted/target output, \(b_0, b_1,... b_n\) are the regression coefficients, x is an independent/ input variable. In simple words, we can say that if data are not distributed linearly, instead it is \(n^\mathrm{th}\) degree of polynomial then we use polynomial regression to get desired output.

LASSO and ridge regression: LASSO and Ridge regression are well known as powerful techniques which are typically used for building learning models in presence of a large number of features, due to their capability to preventing over-fitting and reducing the complexity of the model. The LASSO (least absolute shrinkage and selection operator) regression model uses L 1 regularization technique [ 82 ] that uses shrinkage, which penalizes “absolute value of magnitude of coefficients” ( L 1 penalty). As a result, LASSO appears to render coefficients to absolute zero. Thus, LASSO regression aims to find the subset of predictors that minimizes the prediction error for a quantitative response variable. On the other hand, ridge regression uses L 2 regularization [ 82 ], which is the “squared magnitude of coefficients” ( L 2 penalty). Thus, ridge regression forces the weights to be small but never sets the coefficient value to zero, and does a non-sparse solution. Overall, LASSO regression is useful to obtain a subset of predictors by eliminating less important features, and ridge regression is useful when a data set has “multicollinearity” which refers to the predictors that are correlated with other predictors.

Cluster Analysis

Cluster analysis, also known as clustering, is an unsupervised machine learning technique for identifying and grouping related data points in large datasets without concern for the specific outcome. It does grouping a collection of objects in such a way that objects in the same category, called a cluster, are in some sense more similar to each other than objects in other groups [ 41 ]. It is often used as a data analysis technique to discover interesting trends or patterns in data, e.g., groups of consumers based on their behavior. In a broad range of application areas, such as cybersecurity, e-commerce, mobile data processing, health analytics, user modeling and behavioral analytics, clustering can be used. In the following, we briefly discuss and summarize various types of clustering methods.

Partitioning methods: Based on the features and similarities in the data, this clustering approach categorizes the data into multiple groups or clusters. The data scientists or analysts typically determine the number of clusters either dynamically or statically depending on the nature of the target applications, to produce for the methods of clustering. The most common clustering algorithms based on partitioning methods are K-means [ 69 ], K-Mediods [ 80 ], CLARA [ 55 ] etc.

Density-based methods: To identify distinct groups or clusters, it uses the concept that a cluster in the data space is a contiguous region of high point density isolated from other such clusters by contiguous regions of low point density. Points that are not part of a cluster are considered as noise. The typical clustering algorithms based on density are DBSCAN [ 32 ], OPTICS [ 12 ] etc. The density-based methods typically struggle with clusters of similar density and high dimensionality data.

Hierarchical-based methods: Hierarchical clustering typically seeks to construct a hierarchy of clusters, i.e., the tree structure. Strategies for hierarchical clustering generally fall into two types: (i) Agglomerative—a “bottom-up” approach in which each observation begins in its cluster and pairs of clusters are combined as one, moves up the hierarchy, and (ii) Divisive—a “top-down” approach in which all observations begin in one cluster and splits are performed recursively, moves down the hierarchy, as shown in Fig 7 . Our earlier proposed BOTS technique, Sarker et al. [ 102 ] is an example of a hierarchical, particularly, bottom-up clustering algorithm.

Grid-based methods: To deal with massive datasets, grid-based clustering is especially suitable. To obtain clusters, the principle is first to summarize the dataset with a grid representation and then to combine grid cells. STING [ 122 ], CLIQUE [ 6 ], etc. are the standard algorithms of grid-based clustering.

Model-based methods: There are mainly two types of model-based clustering algorithms: one that uses statistical learning, and the other based on a method of neural network learning [ 130 ]. For instance, GMM [ 89 ] is an example of a statistical learning method, and SOM [ 22 ] [ 96 ] is an example of a neural network learning method.

Constraint-based methods: Constrained-based clustering is a semi-supervised approach to data clustering that uses constraints to incorporate domain knowledge. Application or user-oriented constraints are incorporated to perform the clustering. The typical algorithms of this kind of clustering are COP K-means [ 121 ], CMWK-Means [ 27 ], etc.

A graphical interpretation of the widely-used hierarchical clustering (Bottom-up and top-down) technique

Many clustering algorithms have been proposed with the ability to grouping data in machine learning and data science literature [ 41 , 125 ]. In the following, we summarize the popular methods that are used widely in various application areas.

K-means clustering: K-means clustering [ 69 ] is a fast, robust, and simple algorithm that provides reliable results when data sets are well-separated from each other. The data points are allocated to a cluster in this algorithm in such a way that the amount of the squared distance between the data points and the centroid is as small as possible. In other words, the K-means algorithm identifies the k number of centroids and then assigns each data point to the nearest cluster while keeping the centroids as small as possible. Since it begins with a random selection of cluster centers, the results can be inconsistent. Since extreme values can easily affect a mean, the K-means clustering algorithm is sensitive to outliers. K-medoids clustering [ 91 ] is a variant of K-means that is more robust to noises and outliers.

Mean-shift clustering: Mean-shift clustering [ 37 ] is a nonparametric clustering technique that does not require prior knowledge of the number of clusters or constraints on cluster shape. Mean-shift clustering aims to discover “blobs” in a smooth distribution or density of samples [ 82 ]. It is a centroid-based algorithm that works by updating centroid candidates to be the mean of the points in a given region. To form the final set of centroids, these candidates are filtered in a post-processing stage to remove near-duplicates. Cluster analysis in computer vision and image processing are examples of application domains. Mean Shift has the disadvantage of being computationally expensive. Moreover, in cases of high dimension, where the number of clusters shifts abruptly, the mean-shift algorithm does not work well.

DBSCAN: Density-based spatial clustering of applications with noise (DBSCAN) [ 32 ] is a base algorithm for density-based clustering which is widely used in data mining and machine learning. This is known as a non-parametric density-based clustering technique for separating high-density clusters from low-density clusters that are used in model building. DBSCAN’s main idea is that a point belongs to a cluster if it is close to many points from that cluster. It can find clusters of various shapes and sizes in a vast volume of data that is noisy and contains outliers. DBSCAN, unlike k-means, does not require a priori specification of the number of clusters in the data and can find arbitrarily shaped clusters. Although k-means is much faster than DBSCAN, it is efficient at finding high-density regions and outliers, i.e., is robust to outliers.

GMM clustering: Gaussian mixture models (GMMs) are often used for data clustering, which is a distribution-based clustering algorithm. A Gaussian mixture model is a probabilistic model in which all the data points are produced by a mixture of a finite number of Gaussian distributions with unknown parameters [ 82 ]. To find the Gaussian parameters for each cluster, an optimization algorithm called expectation-maximization (EM) [ 82 ] can be used. EM is an iterative method that uses a statistical model to estimate the parameters. In contrast to k-means, Gaussian mixture models account for uncertainty and return the likelihood that a data point belongs to one of the k clusters. GMM clustering is more robust than k-means and works well even with non-linear data distributions.

Agglomerative hierarchical clustering: The most common method of hierarchical clustering used to group objects in clusters based on their similarity is agglomerative clustering. This technique uses a bottom-up approach, where each object is first treated as a singleton cluster by the algorithm. Following that, pairs of clusters are merged one by one until all clusters have been merged into a single large cluster containing all objects. The result is a dendrogram, which is a tree-based representation of the elements. Single linkage [ 115 ], Complete linkage [ 116 ], BOTS [ 102 ] etc. are some examples of such techniques. The main advantage of agglomerative hierarchical clustering over k-means is that the tree-structure hierarchy generated by agglomerative clustering is more informative than the unstructured collection of flat clusters returned by k-means, which can help to make better decisions in the relevant application areas.

Dimensionality Reduction and Feature Learning

In machine learning and data science, high-dimensional data processing is a challenging task for both researchers and application developers. Thus, dimensionality reduction which is an unsupervised learning technique, is important because it leads to better human interpretations, lower computational costs, and avoids overfitting and redundancy by simplifying models. Both the process of feature selection and feature extraction can be used for dimensionality reduction. The primary distinction between the selection and extraction of features is that the “feature selection” keeps a subset of the original features [ 97 ], while “feature extraction” creates brand new ones [ 98 ]. In the following, we briefly discuss these techniques.

Feature selection: The selection of features, also known as the selection of variables or attributes in the data, is the process of choosing a subset of unique features (variables, predictors) to use in building machine learning and data science model. It decreases a model’s complexity by eliminating the irrelevant or less important features and allows for faster training of machine learning algorithms. A right and optimal subset of the selected features in a problem domain is capable to minimize the overfitting problem through simplifying and generalizing the model as well as increases the model’s accuracy [ 97 ]. Thus, “feature selection” [ 66 , 99 ] is considered as one of the primary concepts in machine learning that greatly affects the effectiveness and efficiency of the target machine learning model. Chi-squared test, Analysis of variance (ANOVA) test, Pearson’s correlation coefficient, recursive feature elimination, are some popular techniques that can be used for feature selection.

Feature extraction: In a machine learning-based model or system, feature extraction techniques usually provide a better understanding of the data, a way to improve prediction accuracy, and to reduce computational cost or training time. The aim of “feature extraction” [ 66 , 99 ] is to reduce the number of features in a dataset by generating new ones from the existing ones and then discarding the original features. The majority of the information found in the original set of features can then be summarized using this new reduced set of features. For instance, principal components analysis (PCA) is often used as a dimensionality-reduction technique to extract a lower-dimensional space creating new brand components from the existing features in a dataset [ 98 ].

Many algorithms have been proposed to reduce data dimensions in the machine learning and data science literature [ 41 , 125 ]. In the following, we summarize the popular methods that are used widely in various application areas.

Variance threshold: A simple basic approach to feature selection is the variance threshold [ 82 ]. This excludes all features of low variance, i.e., all features whose variance does not exceed the threshold. It eliminates all zero-variance characteristics by default, i.e., characteristics that have the same value in all samples. This feature selection algorithm looks only at the ( X ) features, not the ( y ) outputs needed, and can, therefore, be used for unsupervised learning.

Pearson correlation: Pearson’s correlation is another method to understand a feature’s relation to the response variable and can be used for feature selection [ 99 ]. This method is also used for finding the association between the features in a dataset. The resulting value is \([-1, 1]\) , where \(-1\) means perfect negative correlation, \(+1\) means perfect positive correlation, and 0 means that the two variables do not have a linear correlation. If two random variables represent X and Y , then the correlation coefficient between X and Y is defined as [ 41 ]

ANOVA: Analysis of variance (ANOVA) is a statistical tool used to verify the mean values of two or more groups that differ significantly from each other. ANOVA assumes a linear relationship between the variables and the target and the variables’ normal distribution. To statistically test the equality of means, the ANOVA method utilizes F tests. For feature selection, the results ‘ANOVA F value’ [ 82 ] of this test can be used where certain features independent of the goal variable can be omitted.

Chi square: The chi-square \({\chi }^2\) [ 82 ] statistic is an estimate of the difference between the effects of a series of events or variables observed and expected frequencies. The magnitude of the difference between the real and observed values, the degrees of freedom, and the sample size depends on \({\chi }^2\) . The chi-square \({\chi }^2\) is commonly used for testing relationships between categorical variables. If \(O_i\) represents observed value and \(E_i\) represents expected value, then

Recursive feature elimination (RFE): Recursive Feature Elimination (RFE) is a brute force approach to feature selection. RFE [ 82 ] fits the model and removes the weakest feature before it meets the specified number of features. Features are ranked by the coefficients or feature significance of the model. RFE aims to remove dependencies and collinearity in the model by recursively removing a small number of features per iteration.

Model-based selection: To reduce the dimensionality of the data, linear models penalized with the L 1 regularization can be used. Least absolute shrinkage and selection operator (Lasso) regression is a type of linear regression that has the property of shrinking some of the coefficients to zero [ 82 ]. Therefore, that feature can be removed from the model. Thus, the penalized lasso regression method, often used in machine learning to select the subset of variables. Extra Trees Classifier [ 82 ] is an example of a tree-based estimator that can be used to compute impurity-based function importance, which can then be used to discard irrelevant features.

Principal component analysis (PCA): Principal component analysis (PCA) is a well-known unsupervised learning approach in the field of machine learning and data science. PCA is a mathematical technique that transforms a set of correlated variables into a set of uncorrelated variables known as principal components [ 48 , 81 ]. Figure 8 shows an example of the effect of PCA on various dimensions space, where Fig. 8 a shows the original features in 3D space, and Fig. 8 b shows the created principal components PC1 and PC2 onto a 2D plane, and 1D line with the principal component PC1 respectively. Thus, PCA can be used as a feature extraction technique that reduces the dimensionality of the datasets, and to build an effective machine learning model [ 98 ]. Technically, PCA identifies the completely transformed with the highest eigenvalues of a covariance matrix and then uses those to project the data into a new subspace of equal or fewer dimensions [ 82 ].

An example of a principal component analysis (PCA) and created principal components PC1 and PC2 in different dimension space

Association Rule Learning

Association rule learning is a rule-based machine learning approach to discover interesting relationships, “IF-THEN” statements, in large datasets between variables [ 7 ]. One example is that “if a customer buys a computer or laptop (an item), s/he is likely to also buy anti-virus software (another item) at the same time”. Association rules are employed today in many application areas, including IoT services, medical diagnosis, usage behavior analytics, web usage mining, smartphone applications, cybersecurity applications, and bioinformatics. In comparison to sequence mining, association rule learning does not usually take into account the order of things within or across transactions. A common way of measuring the usefulness of association rules is to use its parameter, the ‘support’ and ‘confidence’, which is introduced in [ 7 ].

In the data mining literature, many association rule learning methods have been proposed, such as logic dependent [ 34 ], frequent pattern based [ 8 , 49 , 68 ], and tree-based [ 42 ]. The most popular association rule learning algorithms are summarized below.

AIS and SETM: AIS is the first algorithm proposed by Agrawal et al. [ 7 ] for association rule mining. The AIS algorithm’s main downside is that too many candidate itemsets are generated, requiring more space and wasting a lot of effort. This algorithm calls for too many passes over the entire dataset to produce the rules. Another approach SETM [ 49 ] exhibits good performance and stable behavior with execution time; however, it suffers from the same flaw as the AIS algorithm.

Apriori: For generating association rules for a given dataset, Agrawal et al. [ 8 ] proposed the Apriori, Apriori-TID, and Apriori-Hybrid algorithms. These later algorithms outperform the AIS and SETM mentioned above due to the Apriori property of frequent itemset [ 8 ]. The term ‘Apriori’ usually refers to having prior knowledge of frequent itemset properties. Apriori uses a “bottom-up” approach, where it generates the candidate itemsets. To reduce the search space, Apriori uses the property “all subsets of a frequent itemset must be frequent; and if an itemset is infrequent, then all its supersets must also be infrequent”. Another approach predictive Apriori [ 108 ] can also generate rules; however, it receives unexpected results as it combines both the support and confidence. The Apriori [ 8 ] is the widely applicable techniques in mining association rules.

ECLAT: This technique was proposed by Zaki et al. [ 131 ] and stands for Equivalence Class Clustering and bottom-up Lattice Traversal. ECLAT uses a depth-first search to find frequent itemsets. In contrast to the Apriori [ 8 ] algorithm, which represents data in a horizontal pattern, it represents data vertically. Hence, the ECLAT algorithm is more efficient and scalable in the area of association rule learning. This algorithm is better suited for small and medium datasets whereas the Apriori algorithm is used for large datasets.

FP-Growth: Another common association rule learning technique based on the frequent-pattern tree (FP-tree) proposed by Han et al. [ 42 ] is Frequent Pattern Growth, known as FP-Growth. The key difference with Apriori is that while generating rules, the Apriori algorithm [ 8 ] generates frequent candidate itemsets; on the other hand, the FP-growth algorithm [ 42 ] prevents candidate generation and thus produces a tree by the successful strategy of ‘divide and conquer’ approach. Due to its sophistication, however, FP-Tree is challenging to use in an interactive mining environment [ 133 ]. Thus, the FP-Tree would not fit into memory for massive data sets, making it challenging to process big data as well. Another solution is RARM (Rapid Association Rule Mining) proposed by Das et al. [ 26 ] but faces a related FP-tree issue [ 133 ].

ABC-RuleMiner: A rule-based machine learning method, recently proposed in our earlier paper, by Sarker et al. [ 104 ], to discover the interesting non-redundant rules to provide real-world intelligent services. This algorithm effectively identifies the redundancy in associations by taking into account the impact or precedence of the related contextual features and discovers a set of non-redundant association rules. This algorithm first constructs an association generation tree (AGT), a top-down approach, and then extracts the association rules through traversing the tree. Thus, ABC-RuleMiner is more potent than traditional rule-based methods in terms of both non-redundant rule generation and intelligent decision-making, particularly in a context-aware smart computing environment, where human or user preferences are involved.

Among the association rule learning techniques discussed above, Apriori [ 8 ] is the most widely used algorithm for discovering association rules from a given dataset [ 133 ]. The main strength of the association learning technique is its comprehensiveness, as it generates all associations that satisfy the user-specified constraints, such as minimum support and confidence value. The ABC-RuleMiner approach [ 104 ] discussed earlier could give significant results in terms of non-redundant rule generation and intelligent decision-making for the relevant application areas in the real world.

Reinforcement Learning

Reinforcement learning (RL) is a machine learning technique that allows an agent to learn by trial and error in an interactive environment using input from its actions and experiences. Unlike supervised learning, which is based on given sample data or examples, the RL method is based on interacting with the environment. The problem to be solved in reinforcement learning (RL) is defined as a Markov Decision Process (MDP) [ 86 ], i.e., all about sequentially making decisions. An RL problem typically includes four elements such as Agent, Environment, Rewards, and Policy.

RL can be split roughly into Model-based and Model-free techniques. Model-based RL is the process of inferring optimal behavior from a model of the environment by performing actions and observing the results, which include the next state and the immediate reward [ 85 ]. AlphaZero, AlphaGo [ 113 ] are examples of the model-based approaches. On the other hand, a model-free approach does not use the distribution of the transition probability and the reward function associated with MDP. Q-learning, Deep Q Network, Monte Carlo Control, SARSA (State–Action–Reward–State–Action), etc. are some examples of model-free algorithms [ 52 ]. The policy network, which is required for model-based RL but not for model-free, is the key difference between model-free and model-based learning. In the following, we discuss the popular RL algorithms.

Monte Carlo methods: Monte Carlo techniques, or Monte Carlo experiments, are a wide category of computational algorithms that rely on repeated random sampling to obtain numerical results [ 52 ]. The underlying concept is to use randomness to solve problems that are deterministic in principle. Optimization, numerical integration, and making drawings from the probability distribution are the three problem classes where Monte Carlo techniques are most commonly used.

Q-learning: Q-learning is a model-free reinforcement learning algorithm for learning the quality of behaviors that tell an agent what action to take under what conditions [ 52 ]. It does not need a model of the environment (hence the term “model-free”), and it can deal with stochastic transitions and rewards without the need for adaptations. The ‘Q’ in Q-learning usually stands for quality, as the algorithm calculates the maximum expected rewards for a given behavior in a given state.

Deep Q-learning: The basic working step in Deep Q-Learning [ 52 ] is that the initial state is fed into the neural network, which returns the Q-value of all possible actions as an output. Still, when we have a reasonably simple setting to overcome, Q-learning works well. However, when the number of states and actions becomes more complicated, deep learning can be used as a function approximator.

Reinforcement learning, along with supervised and unsupervised learning, is one of the basic machine learning paradigms. RL can be used to solve numerous real-world problems in various fields, such as game theory, control theory, operations analysis, information theory, simulation-based optimization, manufacturing, supply chain logistics, multi-agent systems, swarm intelligence, aircraft control, robot motion control, and many more.

Artificial Neural Network and Deep Learning

Deep learning is part of a wider family of artificial neural networks (ANN)-based machine learning approaches with representation learning. Deep learning provides a computational architecture by combining several processing layers, such as input, hidden, and output layers, to learn from data [ 41 ]. The main advantage of deep learning over traditional machine learning methods is its better performance in several cases, particularly learning from large datasets [ 105 , 129 ]. Figure 9 shows a general performance of deep learning over machine learning considering the increasing amount of data. However, it may vary depending on the data characteristics and experimental set up.

Machine learning and deep learning performance in general with the amount of data

The most common deep learning algorithms are: Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN, or ConvNet), Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) [ 96 ]. In the following, we discuss various types of deep learning methods that can be used to build effective data-driven models for various purposes.

A structure of an artificial neural network modeling with multiple processing layers

MLP: The base architecture of deep learning, which is also known as the feed-forward artificial neural network, is called a multilayer perceptron (MLP) [ 82 ]. A typical MLP is a fully connected network consisting of an input layer, one or more hidden layers, and an output layer, as shown in Fig. 10 . Each node in one layer connects to each node in the following layer at a certain weight. MLP utilizes the “Backpropagation” technique [ 41 ], the most “fundamental building block” in a neural network, to adjust the weight values internally while building the model. MLP is sensitive to scaling features and allows a variety of hyperparameters to be tuned, such as the number of hidden layers, neurons, and iterations, which can result in a computationally costly model.

CNN or ConvNet: The convolution neural network (CNN) [ 65 ] enhances the design of the standard ANN, consisting of convolutional layers, pooling layers, as well as fully connected layers, as shown in Fig. 11 . As it takes the advantage of the two-dimensional (2D) structure of the input data, it is typically broadly used in several areas such as image and video recognition, image processing and classification, medical image analysis, natural language processing, etc. While CNN has a greater computational burden, without any manual intervention, it has the advantage of automatically detecting the important features, and hence CNN is considered to be more powerful than conventional ANN. A number of advanced deep learning models based on CNN can be used in the field, such as AlexNet [ 60 ], Xception [ 24 ], Inception [ 118 ], Visual Geometry Group (VGG) [ 44 ], ResNet [ 45 ], etc.

LSTM-RNN: Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the area of deep learning [ 38 ]. LSTM has feedback links, unlike normal feed-forward neural networks. LSTM networks are well-suited for analyzing and learning sequential data, such as classifying, processing, and predicting data based on time series data, which differentiates it from other conventional networks. Thus, LSTM can be used when the data are in a sequential format, such as time, sentence, etc., and commonly applied in the area of time-series analysis, natural language processing, speech recognition, etc.

An example of a convolutional neural network (CNN or ConvNet) including multiple convolution and pooling layers

In addition to these most common deep learning methods discussed above, several other deep learning approaches [ 96 ] exist in the area for various purposes. For instance, the self-organizing map (SOM) [ 58 ] uses unsupervised learning to represent the high-dimensional data by a 2D grid map, thus achieving dimensionality reduction. The autoencoder (AE) [ 15 ] is another learning technique that is widely used for dimensionality reduction as well and feature extraction in unsupervised learning tasks. Restricted Boltzmann machines (RBM) [ 46 ] can be used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling. A deep belief network (DBN) is typically composed of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, and a backpropagation neural network (BPNN) [ 123 ]. A generative adversarial network (GAN) [ 39 ] is a form of the network for deep learning that can generate data with characteristics close to the actual data input. Transfer learning is currently very common because it can train deep neural networks with comparatively low data, which is typically the re-use of a new problem with a pre-trained model [ 124 ]. A brief discussion of these artificial neural networks (ANN) and deep learning (DL) models are summarized in our earlier paper Sarker et al. [ 96 ].

Overall, based on the learning techniques discussed above, we can conclude that various types of machine learning techniques, such as classification analysis, regression, data clustering, feature selection and extraction, and dimensionality reduction, association rule learning, reinforcement learning, or deep learning techniques, can play a significant role for various purposes according to their capabilities. In the following section, we discuss several application areas based on machine learning algorithms.

Applications of Machine Learning

In the current age of the Fourth Industrial Revolution (4IR), machine learning becomes popular in various application areas, because of its learning capabilities from the past and making intelligent decisions. In the following, we summarize and discuss ten popular application areas of machine learning technology.

Predictive analytics and intelligent decision-making: A major application field of machine learning is intelligent decision-making by data-driven predictive analytics [ 21 , 70 ]. The basis of predictive analytics is capturing and exploiting relationships between explanatory variables and predicted variables from previous events to predict the unknown outcome [ 41 ]. For instance, identifying suspects or criminals after a crime has been committed, or detecting credit card fraud as it happens. Another application, where machine learning algorithms can assist retailers in better understanding consumer preferences and behavior, better manage inventory, avoiding out-of-stock situations, and optimizing logistics and warehousing in e-commerce. Various machine learning algorithms such as decision trees, support vector machines, artificial neural networks, etc. [ 106 , 125 ] are commonly used in the area. Since accurate predictions provide insight into the unknown, they can improve the decisions of industries, businesses, and almost any organization, including government agencies, e-commerce, telecommunications, banking and financial services, healthcare, sales and marketing, transportation, social networking, and many others.

Cybersecurity and threat intelligence: Cybersecurity is one of the most essential areas of Industry 4.0. [ 114 ], which is typically the practice of protecting networks, systems, hardware, and data from digital attacks [ 114 ]. Machine learning has become a crucial cybersecurity technology that constantly learns by analyzing data to identify patterns, better detect malware in encrypted traffic, find insider threats, predict where bad neighborhoods are online, keep people safe while browsing, or secure data in the cloud by uncovering suspicious activity. For instance, clustering techniques can be used to identify cyber-anomalies, policy violations, etc. To detect various types of cyber-attacks or intrusions machine learning classification models by taking into account the impact of security features are useful [ 97 ]. Various deep learning-based security models can also be used on the large scale of security datasets [ 96 , 129 ]. Moreover, security policy rules generated by association rule learning techniques can play a significant role to build a rule-based security system [ 105 ]. Thus, we can say that various learning techniques discussed in Sect. Machine Learning Tasks and Algorithms , can enable cybersecurity professionals to be more proactive inefficiently preventing threats and cyber-attacks.

Internet of things (IoT) and smart cities: Internet of Things (IoT) is another essential area of Industry 4.0. [ 114 ], which turns everyday objects into smart objects by allowing them to transmit data and automate tasks without the need for human interaction. IoT is, therefore, considered to be the big frontier that can enhance almost all activities in our lives, such as smart governance, smart home, education, communication, transportation, retail, agriculture, health care, business, and many more [ 70 ]. Smart city is one of IoT’s core fields of application, using technologies to enhance city services and residents’ living experiences [ 132 , 135 ]. As machine learning utilizes experience to recognize trends and create models that help predict future behavior and events, it has become a crucial technology for IoT applications [ 103 ]. For example, to predict traffic in smart cities, parking availability prediction, estimate the total usage of energy of the citizens for a particular period, make context-aware and timely decisions for the people, etc. are some tasks that can be solved using machine learning techniques according to the current needs of the people.

Traffic prediction and transportation: Transportation systems have become a crucial component of every country’s economic development. Nonetheless, several cities around the world are experiencing an excessive rise in traffic volume, resulting in serious issues such as delays, traffic congestion, higher fuel prices, increased CO \(_2\) pollution, accidents, emergencies, and a decline in modern society’s quality of life [ 40 ]. Thus, an intelligent transportation system through predicting future traffic is important, which is an indispensable part of a smart city. Accurate traffic prediction based on machine and deep learning modeling can help to minimize the issues [ 17 , 30 , 31 ]. For example, based on the travel history and trend of traveling through various routes, machine learning can assist transportation companies in predicting possible issues that may occur on specific routes and recommending their customers to take a different path. Ultimately, these learning-based data-driven models help improve traffic flow, increase the usage and efficiency of sustainable modes of transportation, and limit real-world disruption by modeling and visualizing future changes.

Healthcare and COVID-19 pandemic: Machine learning can help to solve diagnostic and prognostic problems in a variety of medical domains, such as disease prediction, medical knowledge extraction, detecting regularities in data, patient management, etc. [ 33 , 77 , 112 ]. Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus, according to the World Health Organization (WHO) [ 3 ]. Recently, the learning techniques have become popular in the battle against COVID-19 [ 61 , 63 ]. For the COVID-19 pandemic, the learning techniques are used to classify patients at high risk, their mortality rate, and other anomalies [ 61 ]. It can also be used to better understand the virus’s origin, COVID-19 outbreak prediction, as well as for disease diagnosis and treatment [ 14 , 50 ]. With the help of machine learning, researchers can forecast where and when, the COVID-19 is likely to spread, and notify those regions to match the required arrangements. Deep learning also provides exciting solutions to the problems of medical image processing and is seen as a crucial technique for potential applications, particularly for COVID-19 pandemic [ 10 , 78 , 111 ]. Overall, machine and deep learning techniques can help to fight the COVID-19 virus and the pandemic as well as intelligent clinical decisions making in the domain of healthcare.

E-commerce and product recommendations: Product recommendation is one of the most well known and widely used applications of machine learning, and it is one of the most prominent features of almost any e-commerce website today. Machine learning technology can assist businesses in analyzing their consumers’ purchasing histories and making customized product suggestions for their next purchase based on their behavior and preferences. E-commerce companies, for example, can easily position product suggestions and offers by analyzing browsing trends and click-through rates of specific items. Using predictive modeling based on machine learning techniques, many online retailers, such as Amazon [ 71 ], can better manage inventory, prevent out-of-stock situations, and optimize logistics and warehousing. The future of sales and marketing is the ability to capture, evaluate, and use consumer data to provide a customized shopping experience. Furthermore, machine learning techniques enable companies to create packages and content that are tailored to the needs of their customers, allowing them to maintain existing customers while attracting new ones.

NLP and sentiment analysis: Natural language processing (NLP) involves the reading and understanding of spoken or written language through the medium of a computer [ 79 , 103 ]. Thus, NLP helps computers, for instance, to read a text, hear speech, interpret it, analyze sentiment, and decide which aspects are significant, where machine learning techniques can be used. Virtual personal assistant, chatbot, speech recognition, document description, language or machine translation, etc. are some examples of NLP-related tasks. Sentiment Analysis [ 90 ] (also referred to as opinion mining or emotion AI) is an NLP sub-field that seeks to identify and extract public mood and views within a given text through blogs, reviews, social media, forums, news, etc. For instance, businesses and brands use sentiment analysis to understand the social sentiment of their brand, product, or service through social media platforms or the web as a whole. Overall, sentiment analysis is considered as a machine learning task that analyzes texts for polarity, such as “positive”, “negative”, or “neutral” along with more intense emotions like very happy, happy, sad, very sad, angry, have interest, or not interested etc.

Image, speech and pattern recognition: Image recognition [ 36 ] is a well-known and widespread example of machine learning in the real world, which can identify an object as a digital image. For instance, to label an x-ray as cancerous or not, character recognition, or face detection in an image, tagging suggestions on social media, e.g., Facebook, are common examples of image recognition. Speech recognition [ 23 ] is also very popular that typically uses sound and linguistic models, e.g., Google Assistant, Cortana, Siri, Alexa, etc. [ 67 ], where machine learning methods are used. Pattern recognition [ 13 ] is defined as the automated recognition of patterns and regularities in data, e.g., image analysis. Several machine learning techniques such as classification, feature selection, clustering, or sequence labeling methods are used in the area.

Sustainable agriculture: Agriculture is essential to the survival of all human activities [ 109 ]. Sustainable agriculture practices help to improve agricultural productivity while also reducing negative impacts on the environment [ 5 , 25 , 109 ]. The sustainable agriculture supply chains are knowledge-intensive and based on information, skills, technologies, etc., where knowledge transfer encourages farmers to enhance their decisions to adopt sustainable agriculture practices utilizing the increasing amount of data captured by emerging technologies, e.g., the Internet of Things (IoT), mobile technologies and devices, etc. [ 5 , 53 , 54 ]. Machine learning can be applied in various phases of sustainable agriculture, such as in the pre-production phase - for the prediction of crop yield, soil properties, irrigation requirements, etc.; in the production phase—for weather prediction, disease detection, weed detection, soil nutrient management, livestock management, etc.; in processing phase—for demand estimation, production planning, etc. and in the distribution phase - the inventory management, consumer analysis, etc.

User behavior analytics and context-aware smartphone applications: Context-awareness is a system’s ability to capture knowledge about its surroundings at any moment and modify behaviors accordingly [ 28 , 93 ]. Context-aware computing uses software and hardware to automatically collect and interpret data for direct responses. The mobile app development environment has been changed greatly with the power of AI, particularly, machine learning techniques through their learning capabilities from contextual data [ 103 , 136 ]. Thus, the developers of mobile apps can rely on machine learning to create smart apps that can understand human behavior, support, and entertain users [ 107 , 137 , 140 ]. To build various personalized data-driven context-aware systems, such as smart interruption management, smart mobile recommendation, context-aware smart searching, decision-making that intelligently assist end mobile phone users in a pervasive computing environment, machine learning techniques are applicable. For example, context-aware association rules can be used to build an intelligent phone call application [ 104 ]. Clustering approaches are useful in capturing users’ diverse behavioral activities by taking into account data in time series [ 102 ]. To predict the future events in various contexts, the classification methods can be used [ 106 , 139 ]. Thus, various learning techniques discussed in Sect. “ Machine Learning Tasks and Algorithms ” can help to build context-aware adaptive and smart applications according to the preferences of the mobile phone users.

In addition to these application areas, machine learning-based models can also apply to several other domains such as bioinformatics, cheminformatics, computer networks, DNA sequence classification, economics and banking, robotics, advanced engineering, and many more.

Challenges and Research Directions

Our study on machine learning algorithms for intelligent data analysis and applications opens several research issues in the area. Thus, in this section, we summarize and discuss the challenges faced and the potential research opportunities and future directions.

In general, the effectiveness and the efficiency of a machine learning-based solution depend on the nature and characteristics of the data, and the performance of the learning algorithms. To collect the data in the relevant domain, such as cybersecurity, IoT, healthcare and agriculture discussed in Sect. “ Applications of Machine Learning ” is not straightforward, although the current cyberspace enables the production of a huge amount of data with very high frequency. Thus, collecting useful data for the target machine learning-based applications, e.g., smart city applications, and their management is important to further analysis. Therefore, a more in-depth investigation of data collection methods is needed while working on the real-world data. Moreover, the historical data may contain many ambiguous values, missing values, outliers, and meaningless data. The machine learning algorithms, discussed in Sect “ Machine Learning Tasks and Algorithms ” highly impact on data quality, and availability for training, and consequently on the resultant model. Thus, to accurately clean and pre-process the diverse data collected from diverse sources is a challenging task. Therefore, effectively modifying or enhance existing pre-processing methods, or proposing new data preparation techniques are required to effectively use the learning algorithms in the associated application domain.

To analyze the data and extract insights, there exist many machine learning algorithms, summarized in Sect. “ Machine Learning Tasks and Algorithms ”. Thus, selecting a proper learning algorithm that is suitable for the target application is challenging. The reason is that the outcome of different learning algorithms may vary depending on the data characteristics [ 106 ]. Selecting a wrong learning algorithm would result in producing unexpected outcomes that may lead to loss of effort, as well as the model’s effectiveness and accuracy. In terms of model building, the techniques discussed in Sect. “ Machine Learning Tasks and Algorithms ” can directly be used to solve many real-world issues in diverse domains, such as cybersecurity, smart cities and healthcare summarized in Sect. “ Applications of Machine Learning ”. However, the hybrid learning model, e.g., the ensemble of methods, modifying or enhancement of the existing learning techniques, or designing new learning methods, could be a potential future work in the area.

Thus, the ultimate success of a machine learning-based solution and corresponding applications mainly depends on both the data and the learning algorithms. If the data are bad to learn, such as non-representative, poor-quality, irrelevant features, or insufficient quantity for training, then the machine learning models may become useless or will produce lower accuracy. Therefore, effectively processing the data and handling the diverse learning algorithms are important, for a machine learning-based solution and eventually building intelligent applications.

In this paper, we have conducted a comprehensive overview of machine learning algorithms for intelligent data analysis and applications. According to our goal, we have briefly discussed how various types of machine learning methods can be used for making solutions to various real-world issues. A successful machine learning model depends on both the data and the performance of the learning algorithms. The sophisticated learning algorithms then need to be trained through the collected real-world data and knowledge related to the target application before the system can assist with intelligent decision-making. We also discussed several popular application areas based on machine learning techniques to highlight their applicability in various real-world issues. Finally, we have summarized and discussed the challenges faced and the potential research opportunities and future directions in the area. Therefore, the challenges that are identified create promising research opportunities in the field which must be addressed with effective solutions in various application areas. Overall, we believe that our study on machine learning-based solutions opens up a promising direction and can be used as a reference guide for potential research and applications for both academia and industry professionals as well as for decision-makers, from a technical point of view.

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The standard approach to expert-in-the-loop machine learning is active learning, where, repeatedly, an expert is asked to annotate one or more records and the machine finds a classifier that respects all annotations made until that point. We propose an alternative approach, IQRef , in which the expert iteratively designs a classifier and the machine helps him or her to determine how well it is performing and, importantly, when to stop, by reporting statistics on a fixed, hold-out sample of annotated records. We justify our approach based on prior work giving a theoretical model of how to re-use hold-out data. We compare the two approaches in the context of identifying a cohort of EHRs and examine their strengths and weaknesses through a case study arising from an optometric research problem. We conclude that both approaches are complementary, and we recommend that they both be employed in conjunction to address the problem of cohort identification in health research.

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The Journal of Machine Learning Research (JMLR), established in 2000 , provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

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Pre-trained Gaussian Processes for Bayesian Optimization Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis Yuanxing Chen, Qingzhao Zhang, Shuangge Ma, Kuangnan Fang , 2024. [ abs ][ pdf ][ bib ]

From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data Katharina Proksch, Christoph Alexander Weikamp, Thomas Staudt, Benoit Lelandais, Christophe Zimmer , 2024. [ abs ][ pdf ][ bib ]      [ code ]

PAMI: An Open-Source Python Library for Pattern Mining Uday Kiran Rage, Veena Pamalla, Masashi Toyoda, Masaru Kitsuregawa , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case Arnaud Descours, Arnaud Guillin, Manon Michel, Boris Nectoux , 2024. [ abs ][ pdf ][ bib ]

Risk Measures and Upper Probabilities: Coherence and Stratification Christian Fröhlich, Robert C. Williamson , 2024. [ abs ][ pdf ][ bib ]

Parallel-in-Time Probabilistic Numerical ODE Solvers Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data Shuo-Chieh Huang, Ruey S. Tsay , 2024. [ abs ][ pdf ][ bib ]

Dropout Regularization Versus l2-Penalization in the Linear Model Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber , 2024. [ abs ][ pdf ][ bib ]

Efficient Convex Algorithms for Universal Kernel Learning Aleksandr Talitckii, Brendon Colbert, Matthew M. Peet , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Manifold Learning by Mixture Models of VAEs for Inverse Problems Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto , 2024. [ abs ][ pdf ][ bib ]      [ code ]

An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters Julie Keisler, El-Ghazali Talbi, Sandra Claudel, Gilles Cabriel , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity Laixi Shi, Yuejie Chi , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Grokking phase transitions in learning local rules with gradient descent Bojan Žunkovič, Enej Ilievski , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Unsupervised Tree Boosting for Learning Probability Distributions Naoki Awaya, Li Ma , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Linear Regression With Unmatched Data: A Deconvolution Perspective Mona Azadkia, Fadoua Balabdaoui , 2024. [ abs ][ pdf ][ bib ]

Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit Karl Hajjar, Lénaïc Chizat, Christophe Giraud , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Sharp analysis of power iteration for tensor PCA Yuchen Wu, Kangjie Zhou , 2024. [ abs ][ pdf ][ bib ]

On the Intrinsic Structures of Spiking Neural Networks Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong, Bin Gu, Zhi-Hua Zhou , 2024. [ abs ][ pdf ][ bib ]

Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance Lisha Chen, Heshan Fernando, Yiming Ying, Tianyi Chen , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data Wanli Hong, Shuyang Ling , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang , 2024. [ abs ][ pdf ][ bib ]

Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks Tian-Yi Zhou, Xiaoming Huo , 2024. [ abs ][ pdf ][ bib ]

Differentially Private Topological Data Analysis Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On the Optimality of Misspecified Spectral Algorithms Haobo Zhang, Yicheng Li, Qian Lin , 2024. [ abs ][ pdf ][ bib ]

An Entropy-Based Model for Hierarchical Learning Amir R. Asadi , 2024. [ abs ][ pdf ][ bib ]

Optimal Clustering with Bandit Feedback Junwen Yang, Zixin Zhong, Vincent Y. F. Tan , 2024. [ abs ][ pdf ][ bib ]

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression Youngseok Kim, Wei Wang, Peter Carbonetto, Matthew Stephens , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks Yuval Belfer, Amnon Geifman, Meirav Galun, Ronen Basri , 2024. [ abs ][ pdf ][ bib ]

Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport Martin Slawski, Bodhisattva Sen , 2024. [ abs ][ pdf ][ bib ]

Volterra Neural Networks (VNNs) Siddharth Roheda, Hamid Krim, Bo Jiang , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton , 2024. [ abs ][ pdf ][ bib ]

Bayesian Regression Markets Thomas Falconer, Jalal Kazempour, Pierre Pinson , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Sharpness-Aware Minimization and the Edge of Stability Philip M. Long, Peter L. Bartlett , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, Lijun Zhang , 2024. [ abs ][ pdf ][ bib ]

Multi-Objective Neural Architecture Search by Learning Search Space Partitions Yiyang Zhao, Linnan Wang, Tian Guo , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms Nicolás García Trillos, Anna Little, Daniel McKenzie, James M. Murphy , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Spherical Rotation Dimension Reduction with Geometric Loss Functions Hengrui Luo, Jeremy E. Purvis, Didong Li , 2024. [ abs ][ pdf ][ bib ]

A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks Yuxin Sun, Dong Lao, Anthony Yezzi, Ganesh Sundaramoorthi , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Two is Better Than One: Regularized Shrinkage of Large Minimum Variance Portfolios Taras Bodnar, Nestor Parolya, Erik Thorsen , 2024. [ abs ][ pdf ][ bib ]

Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning Jinchi Chen, Jie Feng, Weiguo Gao, Ke Wei , 2024. [ abs ][ pdf ][ bib ]

Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Cluster-Adaptive Network A/B Testing: From Randomization to Estimation Yang Liu, Yifan Zhou, Ping Li, Feifang Hu , 2024. [ abs ][ pdf ][ bib ]

On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho, Constantine Caramanis , 2024. [ abs ][ pdf ][ bib ]

Optimization-based Causal Estimation from Heterogeneous Environments Mingzhang Yin, Yixin Wang, David M. Blei , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Optimal Locally Private Nonparametric Classification with Public Data Yuheng Ma, Hanfang Yang , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Learning to Warm-Start Fixed-Point Optimization Algorithms Rajiv Sambharya, Georgina Hall, Brandon Amos, Bartolomeo Stellato , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks Yunfei Yang, Ding-Xuan Zhou , 2024. [ abs ][ pdf ][ bib ]

Nonparametric Copula Models for Multivariate, Mixed, and Missing Data Joseph Feldman, Daniel R. Kowal , 2024. [ abs ][ pdf ][ bib ]      [ code ]

An Analysis of Quantile Temporal-Difference Learning Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney , 2024. [ abs ][ pdf ][ bib ]

Conformal Inference for Online Prediction with Arbitrary Distribution Shifts Isaac Gibbs, Emmanuel J. Candès , 2024. [ abs ][ pdf ][ bib ]      [ code ]

More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization Xu Liu, Heng Lian, Jian Huang , 2024. [ abs ][ pdf ][ bib ]

A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment Robert Hu, Dino Sejdinovic, Robin J. Evans , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models Christoph Schultheiss, Peter Bühlmann , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Simple Cycle Reservoirs are Universal Boyu Li, Robert Simon Fong, Peter Tino , 2024. [ abs ][ pdf ][ bib ]

On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling Rong Tang, Yun Yang , 2024. [ abs ][ pdf ][ bib ]

Generalization and Stability of Interpolating Neural Networks with Minimal Width Hossein Taheri, Christos Thrampoulidis , 2024. [ abs ][ pdf ][ bib ]

Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu, Lei Shi , 2024. [ abs ][ pdf ][ bib ]

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao , 2024. [ abs ][ pdf ][ bib ]

Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning Maximilian Hüttenrauch, Gerhard Neumann , 2024. [ abs ][ pdf ][ bib ]

Kernel Thinning Raaz Dwivedi, Lester Mackey , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian , 2024. [ abs ][ pdf ][ bib ]

Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks Yunpeng Zhao, Ning Hao, Ji Zhu , 2024. [ abs ][ pdf ][ bib ]

Statistical Inference for Fairness Auditing John J. Cherian, Emmanuel J. Candès , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning Yiling Xie, Xiaoming Huo , 2024. [ abs ][ pdf ][ bib ]

DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Flexible Bayesian Product Mixture Models for Vector Autoregressions Suprateek Kundu, Joshua Lukemire , 2024. [ abs ][ pdf ][ bib ]

A Variational Approach to Bayesian Phylogenetic Inference Cheng Zhang, Frederick A. Matsen IV , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Fat-Shattering Dimension of k-fold Aggregations Idan Attias, Aryeh Kontorovich , 2024. [ abs ][ pdf ][ bib ]

Unified Binary and Multiclass Margin-Based Classification Yutong Wang, Clayton Scott , 2024. [ abs ][ pdf ][ bib ]

Neural Feature Learning in Function Space Xiangxiang Xu, Lizhong Zheng , 2024. [ abs ][ pdf ][ bib ]      [ code ]

PyGOD: A Python Library for Graph Outlier Detection Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria Tengyuan Liang , 2024. [ abs ][ pdf ][ bib ]

Fixed points of nonnegative neural networks Tomasz J. Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks Fanghui Liu, Leello Dadi, Volkan Cevher , 2024. [ abs ][ pdf ][ bib ]

A Survey on Multi-player Bandits Etienne Boursier, Vianney Perchet , 2024. [ abs ][ pdf ][ bib ]

Transport-based Counterfactual Models Lucas De Lara, Alberto González-Sanz, Nicholas Asher, Laurent Risser, Jean-Michel Loubes , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction Adam Farooq, Yordan P. Raykov, Petar Raykov, Max A. Little , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Topological Node2vec: Enhanced Graph Embedding via Persistent Homology Yasuaki Hiraoka, Yusuke Imoto, Théo Lacombe, Killian Meehan, Toshiaki Yachimura , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length Katerina Hlaváčková-Schindler, Anna Melnykova, Irene Tubikanec , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Representation Learning via Manifold Flattening and Reconstruction Michael Psenka, Druv Pai, Vishal Raman, Shankar Sastry, Yi Ma , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Bagging Provides Assumption-free Stability Jake A. Soloff, Rina Foygel Barber, Rebecca Willett , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Fairness guarantees in multi-class classification with demographic parity Christophe Denis, Romuald Elie, Mohamed Hebiri, François Hu , 2024. [ abs ][ pdf ][ bib ]

Regimes of No Gain in Multi-class Active Learning Gan Yuan, Yunfan Zhao, Samory Kpotufe , 2024. [ abs ][ pdf ][ bib ]

Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls Mochuan Liu, Yuanjia Wang, Haoda Fu, Donglin Zeng , 2024. [ abs ][ pdf ][ bib ]

Margin-Based Active Learning of Classifiers Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice , 2024. [ abs ][ pdf ][ bib ]

Random Subgraph Detection Using Queries Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal , 2024. [ abs ][ pdf ][ bib ]

Classification with Deep Neural Networks and Logistic Loss Zihan Zhang, Lei Shi, Ding-Xuan Zhou , 2024. [ abs ][ pdf ][ bib ]

Spectral learning of multivariate extremes Marco Avella Medina, Richard A Davis, Gennady Samorodnitsky , 2024. [ abs ][ pdf ][ bib ]

Sum-of-norms clustering does not separate nearby balls Alexander Dunlap, Jean-Christophe Mourrat , 2024. [ abs ][ pdf ][ bib ]      [ code ]

An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization Guy Kornowski, Ohad Shamir , 2024. [ abs ][ pdf ][ bib ]

Linear Distance Metric Learning with Noisy Labels Meysam Alishahi, Anna Little, Jeff M. Phillips , 2024. [ abs ][ pdf ][ bib ]      [ code ]

OpenBox: A Python Toolkit for Generalized Black-box Optimization Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Generative Adversarial Ranking Nets Yinghua Yao, Yuangang Pan, Jing Li, Ivor W. Tsang, Xin Yao , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Predictive Inference with Weak Supervision Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi , 2024. [ abs ][ pdf ][ bib ]

Functions with average smoothness: structure, algorithms, and learning Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich , 2024. [ abs ][ pdf ][ bib ]

Differentially Private Data Release for Mixed-type Data via Latent Factor Models Yanqing Zhang, Qi Xu, Niansheng Tang, Annie Qu , 2024. [ abs ][ pdf ][ bib ]

The Non-Overlapping Statistical Approximation to Overlapping Group Lasso Mingyu Qi, Tianxi Li , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Faster Rates of Differentially Private Stochastic Convex Optimization Jinyan Su, Lijie Hu, Di Wang , 2024. [ abs ][ pdf ][ bib ]

Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization O. Deniz Akyildiz, Sotirios Sabanis , 2024. [ abs ][ pdf ][ bib ]

Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits Junpei Komiyama, Edouard Fouché, Junya Honda , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Stable Implementation of Probabilistic ODE Solvers Nicholas Krämer, Philipp Hennig , 2024. [ abs ][ pdf ][ bib ]

More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity Borja Rodríguez-Gálvez, Ragnar Thobaben, Mikael Skoglund , 2024. [ abs ][ pdf ][ bib ]

Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space Zhengdao Chen , 2024. [ abs ][ pdf ][ bib ]

QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration Felix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Random Forest Weighted Local Fréchet Regression with Random Objects Rui Qiu, Zhou Yu, Ruoqing Zhu , 2024. [ abs ][ pdf ][ bib ]      [ code ]

PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need? Roel Bouman, Zaharah Bukhsh, Tom Heskes , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini , 2024. [ abs ][ pdf ][ bib ]

Information Processing Equalities and the Information–Risk Bridge Robert C. Williamson, Zac Cranko , 2024. [ abs ][ pdf ][ bib ]

Nonparametric Regression for 3D Point Cloud Learning Xinyi Li, Shan Yu, Yueying Wang, Guannan Wang, Li Wang, Ming-Jun Lai , 2024. [ abs ][ pdf ][ bib ]      [ code ]

AMLB: an AutoML Benchmark Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Materials Discovery using Max K-Armed Bandit Nobuaki Kikkawa, Hiroshi Ohno , 2024. [ abs ][ pdf ][ bib ]

Semi-supervised Inference for Block-wise Missing Data without Imputation Shanshan Song, Yuanyuan Lin, Yong Zhou , 2024. [ abs ][ pdf ][ bib ]

Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou , 2024. [ abs ][ pdf ][ bib ]

Scaling Speech Technology to 1,000+ Languages Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli , 2024. [ abs ][ pdf ][ bib ]      [ code ]

MAP- and MLE-Based Teaching Hans Ulrich Simon, Jan Arne Telle , 2024. [ abs ][ pdf ][ bib ]

A General Framework for the Analysis of Kernel-based Tests Tamara Fernández, Nicolás Rivera , 2024. [ abs ][ pdf ][ bib ]

Overparametrized Multi-layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent Jiaming Xu, Hanjing Zhu , 2024. [ abs ][ pdf ][ bib ]

Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces Rui Wang, Yuesheng Xu, Mingsong Yan , 2024. [ abs ][ pdf ][ bib ]

Exploration of the Search Space of Gaussian Graphical Models for Paired Data Alberto Roverato, Dung Ngoc Nguyen , 2024. [ abs ][ pdf ][ bib ]

The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy , 2024. [ abs ][ pdf ][ bib ]

Minimax Rates for High-Dimensional Random Tessellation Forests Eliza O'Reilly, Ngoc Mai Tran , 2024. [ abs ][ pdf ][ bib ]

Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz, Jian Huang , 2024. [ abs ][ pdf ][ bib ]

Spatial meshing for general Bayesian multivariate models Michele Peruzzi, David B. Dunson , 2024. [ abs ][ pdf ][ bib ]      [ code ]

A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables Wei Luo, Yeying Zhu, Xuekui Zhang, Lin Lin , 2024. [ abs ][ pdf ][ bib ]

Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport Ricardo Baptista, Rebecca Morrison, Olivier Zahm, Youssef Marzouk , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On the Learnability of Out-of-distribution Detection Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu , 2024. [ abs ][ pdf ][ bib ]

Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains Yicheng Li, Zixiong Yu, Guhan Chen, Qian Lin , 2024. [ abs ][ pdf ][ bib ]

Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions Maksim Velikanov, Dmitry Yarotsky , 2024. [ abs ][ pdf ][ bib ]

ptwt - The PyTorch Wavelet Toolbox Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Choosing the Number of Topics in LDA Models – A Monte Carlo Comparison of Selection Criteria Victor Bystrov, Viktoriia Naboka-Krell, Anna Staszewska-Bystrova, Peter Winker , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Functional Directed Acyclic Graphs Kuang-Yao Lee, Lexin Li, Bing Li , 2024. [ abs ][ pdf ][ bib ]

Unlabeled Principal Component Analysis and Matrix Completion Yunzhen Yao, Liangzu Peng, Manolis C. Tsakiris , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Distributed Estimation on Semi-Supervised Generalized Linear Model Jiyuan Tu, Weidong Liu, Xiaojun Mao , 2024. [ abs ][ pdf ][ bib ]

Towards Explainable Evaluation Metrics for Machine Translation Christoph Leiter, Piyawat Lertvittayakumjorn, Marina Fomicheva, Wei Zhao, Yang Gao, Steffen Eger , 2024. [ abs ][ pdf ][ bib ]

Differentially private methods for managing model uncertainty in linear regression Víctor Peña, Andrés F. Barrientos , 2024. [ abs ][ pdf ][ bib ]

Data Summarization via Bilevel Optimization Zalán Borsos, Mojmír Mutný, Marco Tagliasacchi, Andreas Krause , 2024. [ abs ][ pdf ][ bib ]

Pareto Smoothed Importance Sampling Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Policy Gradient Methods in the Presence of Symmetries and State Abstractions Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Scaling Instruction-Finetuned Language Models Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei , 2024. [ abs ][ pdf ][ bib ]

Tangential Wasserstein Projections Florian Gunsilius, Meng Hsuan Hsieh, Myung Jin Lee , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Learnability of Linear Port-Hamiltonian Systems Juan-Pablo Ortega, Daiying Yin , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning Ariyan Bighashdel, Daan de Geus, Pavol Jancura, Gijs Dubbelman , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On Unbiased Estimation for Partially Observed Diffusions Jeremy Heng, Jeremie Houssineau, Ajay Jasra , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions Stanislas Ducotterd, Alexis Goujon, Pakshal Bohra, Dimitris Perdios, Sebastian Neumayer, Michael Unser , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Mathematical Framework for Online Social Media Auditing Wasim Huleihel, Yehonathan Refael , 2024. [ abs ][ pdf ][ bib ]

An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates Jessie Finocchiaro, Rafael M. Frongillo, Bo Waggoner , 2024. [ abs ][ pdf ][ bib ]

Low-rank Variational Bayes correction to the Laplace method Janet van Niekerk, Haavard Rue , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Scaling the Convex Barrier with Sparse Dual Algorithms Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Causal-learn: Causal Discovery in Python Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics Noga Mudrik, Yenho Chen, Eva Yezerets, Christopher J. Rozell, Adam S. Charles , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification Natalie S. Frank, Jonathan Niles-Weed , 2024. [ abs ][ pdf ][ bib ]

Data Thinning for Convolution-Closed Distributions Anna Neufeld, Ameer Dharamshi, Lucy L. Gao, Daniela Witten , 2024. [ abs ][ pdf ][ bib ]      [ code ]

A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity Jiang Hu, Kangkang Deng, Jiayuan Wu, Quanzheng Li , 2024. [ abs ][ pdf ][ bib ]

Revisiting RIP Guarantees for Sketching Operators on Mixture Models Ayoub Belhadji, Rémi Gribonval , 2024. [ abs ][ pdf ][ bib ]

Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization Daniel LeJeune, Jiayu Liu, Reinhard Heckel , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks Dong-Young Lim, Sotirios Sabanis , 2024. [ abs ][ pdf ][ bib ]

Axiomatic effect propagation in structural causal models Raghav Singal, George Michailidis , 2024. [ abs ][ pdf ][ bib ]

Optimal First-Order Algorithms as a Function of Inequalities Chanwoo Park, Ernest K. Ryu , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Resource-Efficient Neural Networks for Embedded Systems Wolfgang Roth, Günther Schindler, Bernhard Klein, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani , 2024. [ abs ][ pdf ][ bib ]

Trained Transformers Learn Linear Models In-Context Ruiqi Zhang, Spencer Frei, Peter L. Bartlett , 2024. [ abs ][ pdf ][ bib ]

Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh , 2024. [ abs ][ pdf ][ bib ]

Efficient Modality Selection in Multimodal Learning Yifei He, Runxiang Cheng, Gargi Balasubramaniam, Yao-Hung Hubert Tsai, Han Zhao , 2024. [ abs ][ pdf ][ bib ]

A Multilabel Classification Framework for Approximate Nearest Neighbor Search Ville Hyvönen, Elias Jääsaari, Teemu Roos , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization Lorenzo Pacchiardi, Rilwan A. Adewoyin, Peter Dueben, Ritabrata Dutta , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Multiple Descent in the Multiple Random Feature Model Xuran Meng, Jianfeng Yao, Yuan Cao , 2024. [ abs ][ pdf ][ bib ]

Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling Ye He, Tyler Farghly, Krishnakumar Balasubramanian, Murat A. Erdogdu , 2024. [ abs ][ pdf ][ bib ]

Invariant and Equivariant Reynolds Networks Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Personalized PCA: Decoupling Shared and Unique Features Naichen Shi, Raed Al Kontar , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee George H. Chen , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel , 2024. [ abs ][ pdf ][ bib ]

Convergence for nonconvex ADMM, with applications to CT imaging Rina Foygel Barber, Emil Y. Sidky , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms T. Tony Cai, Hongji Wei , 2024. [ abs ][ pdf ][ bib ]

Sparse NMF with Archetypal Regularization: Computational and Robustness Properties Kayhan Behdin, Rahul Mazumder , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Deep Network Approximation: Beyond ReLU to Diverse Activation Functions Shijun Zhang, Jianfeng Lu, Hongkai Zhao , 2024. [ abs ][ pdf ][ bib ]

Effect-Invariant Mechanisms for Policy Generalization Sorawit Saengkyongam, Niklas Pfister, Predrag Klasnja, Susan Murphy, Jonas Peters , 2024. [ abs ][ pdf ][ bib ]

Pygmtools: A Python Graph Matching Toolkit Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, Junchi Yan , 2024. (Machine Learning Open Source Software Paper) [ abs ][ pdf ][ bib ]      [ code ]

Heterogeneous-Agent Reinforcement Learning Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Sample-efficient Adversarial Imitation Learning Dahuin Jung, Hyungyu Lee, Sungroh Yoon , 2024. [ abs ][ pdf ][ bib ]

Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi , 2024. [ abs ][ pdf ][ bib ]

Rates of convergence for density estimation with generative adversarial networks Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov , 2024. [ abs ][ pdf ][ bib ]

Additive smoothing error in backward variational inference for general state-space models Mathis Chagneux, Elisabeth Gassiat, Pierre Gloaguen, Sylvain Le Corff , 2024. [ abs ][ pdf ][ bib ]

Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality Stephan Wojtowytsch , 2024. [ abs ][ pdf ][ bib ]

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On Tail Decay Rate Estimation of Loss Function Distributions Etrit Haxholli, Marco Lorenzi , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao , 2024. [ abs ][ pdf ][ bib ]

Post-Regularization Confidence Bands for Ordinary Differential Equations Xiaowu Dai, Lexin Li , 2024. [ abs ][ pdf ][ bib ]

On the Generalization of Stochastic Gradient Descent with Momentum Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang , 2024. [ abs ][ pdf ][ bib ]

Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension Shotaro Yagishita, Jun-ya Gotoh , 2024. [ abs ][ pdf ][ bib ]

Iterate Averaging in the Quest for Best Test Error Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Nonparametric Inference under B-bits Quantization Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang , 2024. [ abs ][ pdf ][ bib ]

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box Ryan Giordano, Martin Ingram, Tamara Broderick , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On Sufficient Graphical Models Bing Li, Kyongwon Kim , 2024. [ abs ][ pdf ][ bib ]

Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond Nathan Kallus, Xiaojie Mao, Masatoshi Uehara , 2024. [ abs ][ pdf ][ bib ]      [ code ]

On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks Sebastian Neumayer, Lénaïc Chizat, Michael Unser , 2024. [ abs ][ pdf ][ bib ]

Improving physics-informed neural networks with meta-learned optimization Alex Bihlo , 2024. [ abs ][ pdf ][ bib ]

A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent Stefan Ankirchner, Stefan Perko , 2024. [ abs ][ pdf ][ bib ]

Critically Assessing the State of the Art in Neural Network Verification Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn , 2024. [ abs ][ pdf ][ bib ]

Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov , 2024. [ abs ][ pdf ][ bib ]

Modeling Random Networks with Heterogeneous Reciprocity Daniel Cirkovic, Tiandong Wang , 2024. [ abs ][ pdf ][ bib ]

Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment Zixian Yang, Xin Liu, Lei Ying , 2024. [ abs ][ pdf ][ bib ]

On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Decorrelated Variable Importance Isabella Verdinelli, Larry Wasserman , 2024. [ abs ][ pdf ][ bib ]

Model-Free Representation Learning and Exploration in Low-Rank MDPs Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal , 2024. [ abs ][ pdf ][ bib ]

Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method Ernesto Araya, Guillaume Braun, Hemant Tyagi , 2024. [ abs ][ pdf ][ bib ]      [ code ]

Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization Shicong Cen, Yuting Wei, Yuejie Chi , 2024. [ abs ][ pdf ][ bib ]

Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic Zheng Tracy Ke, Jun S. Liu, Yucong Ma , 2024. [ abs ][ pdf ][ bib ]

Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction Yuze Han, Guangzeng Xie, Zhihua Zhang , 2024. [ abs ][ pdf ][ bib ]

On Truthing Issues in Supervised Classification Jonathan K. Su , 2024. [ abs ][ pdf ][ bib ]

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Machine Learning

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Machine learning articles within Scientific Reports

Article 18 December 2024 | Open Access

Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy

• Lakshay Arora
• , Sunil K. Singh
•  &  Brij B. Gupta

Article 05 December 2024 | Open Access

Robust modelling of arterial blood pressure reconstruction from photoplethysmography

• Jiating Pan
• , Lishi Liang
•  &  Jianming Zhu

Monitoring individualized glucose levels predicts risk for bradycardia in type 2 diabetes patients with chronic kidney disease: a pilot study

• Pejman Farhadi Ghalati
• , Moein E. Samadi
•  &  Andreas Schuppert

AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer’s disease using high-throughput sequencing data

• Rohit Shukla
•  &  Tiratha Raj Singh

Article 03 December 2024 | Open Access

Application of machine learning in breast cancer survival prediction using a multimethod approach

• Seyedeh Zahra Hamedi
• , Hassan Emami
•  &  Vahid Zangouri

Article 02 December 2024 | Open Access

A feasibility study using quantitative and interpretable histological analyses of celiac disease for automated cell type and tissue area classification

• Michael Griffin
• , Aaron M. Gruver
•  &  Klaus Gottlieb

Using deep learning and word embeddings for predicting human agreeableness behavior

• Raed Alsini
•  &  Muhammad Ramzan

Article 30 November 2024 | Open Access

Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks

• Lingkai Tang
• , Lilian M. N. Kebaya
•  &  Emma G. Duerden

Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement

• Junfeng Wang
• , Shenghui Huang
•  &  Yingxu Qiao

Article 29 November 2024 | Open Access

U-shape-based network for left ventricular segmentation in echocardiograms with contrastive pretraining

• Zhengkun Qian
•  &  Zizhong Yang

A multi-level thresholding image segmentation algorithm based on equilibrium optimizer

•  &  Jeng-Shyang Pan

Article 28 November 2024 | Open Access

Innovative multi-class segmentation for brain tumor MRI using noise diffusion probability models and enhancing tumor boundary recognition

• Zengxin Liu
• , Caiwen Ma
•  &  Meilin Xie

THGB: predicting ligand-receptor interactions by combining tree boosting and histogram-based gradient boosting

• Liqian Zhou
• , Jiao Song
•  &  Wenyan Guo

A hypergraph cell membrane computing network model for soybean disease identification

• Yourui Huang
• , Hongping Song
•  &  Xiaoqiao Wang

Article 26 November 2024 | Open Access

Extraction and evaluation of features of preterm patent ductus arteriosus in chest X-ray images using deep learning

• Phillip Chang
• , Hyeon Sung Choi
•  &  Hyun Ho Kim

TPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demand

• Samarth Godara
• , Shbana Begam
•  &  Ravi Nirmal

Article 25 November 2024 | Open Access

ParaAntiProt provides paratope prediction using antibody and protein language models

• Mahmood Kalemati
• , Alireza Noroozi
•  &  Somayyeh Koohi

Article 24 November 2024 | Open Access

Fusing multiplex heterogeneous networks using graph attention-aware fusion networks

• Ziynet Nesibe Kesimoglu
•  &  Serdar Bozdag

Article 23 November 2024 | Open Access

Risk assessment and automatic identification of autistic children based on appearance

• Ruisheng Ran
• , Wei Liang
•  &  Chenyi Liu

HDBind: encoding of molecular structure with hyperdimensional binary representations

• Derek Jones
• , Xiaohua Zhang
•  &  Tajana S. Rosing

Article 22 November 2024 | Open Access

A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems

• Guillaume Canat
• , Antonin Duval
•  &  Alexandra Boussommier-Calleja

Article 21 November 2024 | Open Access

Stage-based colorectal cancer prediction on uncertain dataset using rough computing and LSTM models

•  &  A. Anitha

Article 20 November 2024 | Open Access

An improved water strider algorithm for solving the inverse Burgers Huxley equation

• Hassan Dana Mazraeh
• , Kourosh Parand
•  &  Vladimír Nulíček

Machine learning identifies cytokine signatures of disease severity and autoantibody profiles in systemic lupus erythematosus – a pilot study

• Sarit Sekhar Pattanaik
• , Bidyut Kumar Das
•  &  Ratnadeep Mukherjee

Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities

• Sean Farrell
• , Katharine Anderson
•  &  Noura Al Moubayed

Article 19 November 2024 | Open Access

A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy

• , Qingkun Fan
•  &  Chunzi Liang

Experts fail to reliably detect AI-generated histological data

• Jan Hartung
• , Stefanie Reuter
•  &  Ralf Mrowka

Quantifying spontaneous infant movements using state-space models

• E. Passmore
• , A. K. L. Kwong
•  &  G. Ball

Article 18 November 2024 | Open Access

Generating and evaluating synthetic data in digital pathology through diffusion models

• Matteo Pozzi
• , Shahryar Noei
•  &  Giuseppe Jurman

Integrated analysis of single-cell RNA sequencing and bulk transcriptome data identifies a pyroptosis-associated diagnostic model for Parkinson’s disease

• , Yidan Qin
•  &  Jiajun Chen

An effective robot selection and recharge scheduling approach for improving robotic networks performance

• Shimaa E. ElSayyad
• , Ahmed I. Saleh
•  &  Mohamed M. Abdelsalam

Article 17 November 2024 | Open Access

Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device

• Mithun Kanchan
• , Prasad Kisan Tambe
•  &  Omkar S Powar

DTASUnet: a local and global dual transformer with the attention supervision U-network for brain tumor segmentation

•  &  Gang Yu

Article 16 November 2024 | Open Access

Anti-VEGF treatment outcome prediction based on optical coherence tomography images in neovascular age-related macular degeneration using a deep neural network

• Jeong Mo Han
• , Jinyoung Han
•  &  Daniel Duck-Jin Hwang

NO classifier prediction of anti neuroinflammatory agents using text mining of 3D molecular fingerprints

• , Sangjin Ahn
•  &  Mi-hyun Kim

Article 14 November 2024 | Open Access

Using data from cue presentations results in grossly overestimating semantic BCI performance

• Milan Rybář
• , Riccardo Poli
•  &  Ian Daly

A multimodal fusion network based on a cross-attention mechanism for the classification of Parkinsonian tremor and essential tremor

• , Qianyuan Hu
•  &  Chengli Song

Robust remote detection of depressive tendency based on keystroke dynamics and behavioural characteristics

• , Aamna AlShehhi
•  &  Leontios Hadjileontiadis

Article 13 November 2024 | Open Access

The PERMIT guidelines for designing and implementing all stages of personalised medicine research

• Paula Garcia
• , Rita Banzi
•  &  Jacques Demotes

Article 12 November 2024 | Open Access

Pan-cancer landscape of DCTPP1 and preliminary exploration of DCTPP1 in renal clear cell carcinoma

•  &  Yu Lun

Comparison of dimensionality reduction methods on hyperspectral images for the identification of heathlands and mires

• Anna Jarocińska
• , Dominik Kopeć
•  &  Marlena Kycko

SNPs and blood inflammatory marker featured machine learning for predicting the efficacy of fluorouracil-based chemotherapy in colorectal cancer

• , Wenxin Zhang
•  &  Tianxiao Wang

Article 11 November 2024 | Open Access

Novel metrics for tracking blood pressure changes incontinuous cuffless blood pressure estimations

•  &  Miodrag Bolić

Prediction of miRNA-disease association based on multisource inductive matrix completion

•  &  ZhiXiang Yin

Continuous sign language recognition algorithm based on object detection and variable-length coding sequence

•  &  Changzhi Lv

Article 10 November 2024 | Open Access

Research on stock prediction based on CED-PSO-StockNet time series model

• Xinying Chen
• , Fengjiao Yang
•  &  Weiguo Yi

Exploring the mechanisms of chronic obstructive pulmonary disease and Crohn’s disease: a bioinformatics-based study

• Xinxin Zhang
• , Caiping Liu
•  &  Yue Zhou

Article 09 November 2024 | Open Access

QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals

• Veysel Yusuf Cambay
• , Irem Tasci
•  &  Turker Tuncer

Article 08 November 2024 | Open Access

Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization

• , Tianxiang Song
•  &  Kan Liu

A hybrid local-global neural network for visual classification using raw EEG signals

• Shuning Xue
•  &  Jing Liu

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