The first book of its kind to review the current status andfuture direction of the exciting new branch of machinelearning/data mining called imbalanced learningImbalanced learning focuses on how an intelligent system canlearn when it is provided with imbalanced data. Solving imbalancedlearning problems is critical in numerous data-intensive networkedsystems, including surveillance, security, Internet, finance,biomedical, defense, and more. Due to the inherent complexcharacteristics of imbalanced data sets, learning from such datarequires new understandings, principles, algorithms, and tools totransform vast amounts of raw data efficiently into information andknowledge representation.The first comprehensive look at this new branch of machinelearning, this book offers a critical review of the problem ofimbalanced learning, covering the state of the art in techniques,principles, and real-world applications. Featuring contributionsfrom experts in both academia and industry, Imbalanced Learning:Foundations, Algorithms, and Applications provides chaptercoverage on:Foundations of Imbalanced LearningImbalanced Datasets: From Sampling to ClassifiersEnsemble Methods for Class Imbalance LearningClass Imbalance Learning Methods for Support VectorMachinesClass Imbalance and Active LearningNonstationary Stream Data Learning with Imbalanced ClassDistributionAssessment Metrics for Imbalanced LearningImbalanced Learning: Foundations, Algorithms, andApplications will help scientists and engineers learn how totackle the problem of learning from imbalanced datasets, and gaininsight into current developments in the field as well as futureresearch directions.
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