Imbalanced Learning

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