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 Sd 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.