He / Ma Imbalanced Learning

Foundations, Algorithms, and Applications
1. Auflage 2013
ISBN: 978-1-118-64620-5
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 216 Seiten, E-Book

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.

Autoren/Hrsg.

He, Haibo

Ma, Yunqian

Inhaltsverzeichnis

Preface ix
Contributors xi
1 Introduction 1
Haibo He
1.1 Problem Formulation, 1
1.2 State-of-the-Art Research, 3
1.3 Looking Ahead: Challenges and Opportunities, 6
1.4 Acknowledgments, 7
References, 8
2 Foundations of Imbalanced Learning 13
Gary M. Weiss
2.1 Introduction, 14
2.2 Background, 14
2.3 Foundational Issues, 19
2.4 Methods for Addressing Imbalanced Data, 26
2.5 Mapping Foundational Issues to Solutions, 35
2.6 Misconceptions About Sampling Methods, 36
2.7 Recommendations and Guidelines, 38
References, 38
3 Imbalanced Datasets: From Sampling to Classifiers43
T. Ryan Hoens and Nitesh V. Chawla
3.1 Introduction, 43
3.2 Sampling Methods, 44
3.3 Skew-Insensitive Classifiers for Class Imbalance, 49
3.4 Evaluation Metrics, 52
3.5 Discussion, 56
References, 57
4 Ensemble Methods for Class Imbalance Learning 61
Xu-Ying Liu and Zhi-Hua Zhou
4.1 Introduction, 61
4.2 Ensemble Methods, 62
4.3 Ensemble Methods for Class Imbalance Learning, 66
4.4 Empirical Study, 73
4.5 Concluding Remarks, 79
References, 80
5 Class Imbalance Learning Methods for Support VectorMachines 83
Rukshan Batuwita and Vasile Palade
5.1 Introduction, 83
5.2 Introduction to Support Vector Machines, 84
5.3 SVMs and Class Imbalance, 86
5.4 External Imbalance Learning Methods for SVMs: DataPreprocessing Methods, 87
5.5 Internal Imbalance Learning Methods for SVMs: AlgorithmicMethods, 88
5.6 Summary, 96
References, 96
6 Class Imbalance and Active Learning 101
Josh Attenberg and S¸eyda Ertekin
6.1 Introduction, 102
6.2 Active Learning for Imbalanced Problems, 103
6.3 Active Learning for Imbalanced Data Classification, 110
6.4 Adaptive Resampling with Active Learning, 122
6.5 Difficulties with Extreme Class Imbalance, 129
6.6 Dealing with Disjunctive Classes, 130
6.7 Starting Cold, 132
6.8 Alternatives to Active Learning for Imbalanced Problems,133
6.9 Conclusion, 144
References, 145
7 Nonstationary Stream Data Learning with Imbalanced ClassDistribution 151
Sheng Chen and Haibo He
7.1 Introduction, 152
7.2 Preliminaries, 154
7.3 Algorithms, 157
7.4 Simulation, 167
7.5 Conclusion, 182
7.6 Acknowledgments, 183
References, 184
8 Assessment Metrics for Imbalanced Learning 187
Nathalie Japkowicz
8.1 Introduction, 187
8.2 A Review of Evaluation Metric Families and theirApplicability
to the Class Imbalance Problem, 189
8.3 Threshold Metrics: Multiple- Versus Single-Class Focus,190
8.4 Ranking Methods and Metrics: Taking Uncertainty intoConsideration, 196
8.5 Conclusion, 204
8.6 Acknowledgments, 205
References, 205
Index 207

Über Autor(innen)

HAIBO HE, PhD, is an Associate Professor in theDepartment of Electrical, Computer, and Biomedical Engineering atthe University of Rhode Island. He received the National ScienceFoundation (NSF) CAREER Award and Providence Business News (PBN)Rising Star Innovator Award.
YUNQIAN MA PhD, is a senior principal research scientistof Honeywell Labs at Honeywell Inter-national, Inc. He received theInternational Neural Network Society (INNS) Young InvestigatorAward.