Imbalanced Learning: Foundations, Algorithms, and Applications

By Haibo He, Yunqian Ma. 2013

911 citations recorded by the source corpus.

The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learningImbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on:Foundations of Imbalanced LearningImbalanced Datasets: From Sampling to ClassifiersEnsemble Methods for Class Imbalance LearningClass Imbalance Learning Methods for Support Vector MachinesClass Imbalance and Active LearningNonstationary Stream Data Learning with Imbalanced Class DistributionAssessment Metrics for Imbalanced LearningImbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0628fdf728d0aba31be803a7d834c7f4b569408d

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In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “For an in-depth analysis of other issues related to unbalanced classification we invite the reader to have a look at [87].”
MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation (2015, background): “A general introduction and additional details about these approaches can be found in [39].”
A Survey of Predictive Modeling on Imbalanced Domains (2016, background): “This is an important issue whose resolution could provide a common baseline for comparison of different solutions in a fair and uniﬁed way [He and Ma 2013].”
A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining (2023, background): “To mitigate the class imbalance problem, researchers have developed imbalance learning techniques to process and extract information from data with a severely skewed distribution [4].”
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, methodology): “To cope with the class imbalance problem of the dataset we used as metric the macro average accuracy [57].”
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss (2019, background): “The two common approaches for learning long-tailed data are re-weighting the losses of the examples and re-sampling the examples in the SGD mini-batch (see [5, 21, 10, 17, 18, 9] and the references therein).”
Deep Imbalanced Learning for Face Recognition and Attribute Prediction (2018, background): “mainly divided into two groups: data re-sampling [5], [6], [7], [9], [10], [11], [15] and cost-sensitive learning [8], [12], [13], [14], [16].”
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, background): “In the current ﬁeld of machine learning, the class skewness in data has caused many scholars to pay attention to class-imbalanced learning [12], [13].”
Learning Deep Features for One-Class Classification (2018, background): “The objective of one-class classification is to recognize instances of a concept by only using examples of the same concept [16] as shown in Figure 1.”
Ensemble learning for data stream analysis: A survey (2017, background): “It has been more studied in this static framework and many new algorithms have already been introduced, for their comprehensive review see the recent monograph [74] or surveys [9, 73, 103].”
Learning from imbalanced data: open challenges and future directions (2016, background): “A more recent book in a form of paper collection was edited by He and Ma [25], covering such important issues as sampling strategies, active learning and streaming data.”
Can deep learning beat numerical weather prediction? (2021, background): “While the subject of classifying imbalanced data has received considerable attention (cf. [104]), there appears to be little research on dealing with imbalanced sample sizes in regression problems [105].”