Imbalanced Learning

Beschreibung

The first book of its kind to review the current status andfuture direction of the exciting new branch of machinelearning/data mining called imbalanced learning Imbalanced 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 Learning * Imbalanced Datasets: From Sampling to Classifiers * Ensemble Methods for Class Imbalance Learning * Class Imbalance Learning Methods for Support VectorMachines * Class Imbalance and Active Learning * Nonstationary Stream Data Learning with Imbalanced ClassDistribution * Assessment Metrics for Imbalanced Learning Imbalanced 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.

Autor*in

Haibo He

Themen in »Imbalanced Learning«

Computer Science Database & Data Warehousing Technologies Datenbanken u. Data Warehousing Electrical & Electronics Engineering Elektrotechnik u. Elektronik Informatik Intelligent Systems & Agents Intelligente Systeme Intelligente Systeme u. Agenten Neural Networks Neuronale Netze

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"This book certainly qualifies as a reference for graduatestudies in machine learning. Research students are sure to find ithighly valuable and a prized possession, especially taking intoaccount the wealth of supporting literature that the authors havebrought to the fore." (Computing Reviews, 27March 2014)
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