Imbalanced Learning Foundations, Algorithms, and Applications

by He, Haibo; Ma, Yunqian

• ISBN13: 9781118074626
• ISBN10: 1118074629
• Edition: 1st
• Format: Hardcover
• Copyright: 2013-07-01
• Publisher: Wiley-IEEE Press

Summary

Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few. The first comprehensive look at this new branch of machine learning, this volume offers a critical review of the problem of imbalanced learning, covering the state-of-the-art in techniques, principles, and real-world applications. Scientists and engineers will 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 direction.

Author Biography

HAIBO HE, PhD, is an Associate Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island. He received the National Science Foundation (NSF) CAREER Award and Providence Business News (PBN) Rising Star Innovator Award.

YUNQIAN MA PhD, is a senior principal research scientist of Honeywell Labs at Honeywell Inter-national, Inc. He received the International Neural Network Society (INNS) Young Investigator Award.

Table of Contents

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 Classifiers 43
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 Vector Machines 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: Data Preprocessing Methods, 87

5.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods, 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 Class Distribution 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 their Applicability

to the Class Imbalance Problem, 189

8.3 Threshold Metrics: Multiple- Versus Single-Class Focus, 190

8.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration, 196

8.5 Conclusion, 204

8.6 Acknowledgments, 205

References, 205

Index 207