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.

“This book certainly qualifies as a reference for graduate studies in machine learning. Research students are sure to find it highly valuable and a prized possession, especially taking into account the wealth of supporting literature that the authors have brought to the fore.”  (Computing Reviews, 27 March 2014)

• Preface ixContributors xi1 Introduction 1Haibo He1.1 Problem Formulation 11.2 State-of-the-Art Research 31.3 Looking Ahead: Challenges and Opportunities 61.4 Acknowledgments 7References 82 Foundations of Imbalanced Learning 13Gary M. Weiss2.1 Introduction 142.2 Background 142.3 Foundational Issues 192.4 Methods for Addressing Imbalanced Data 262.5 Mapping Foundational Issues to Solutions 352.6 Misconceptions About Sampling Methods 362.7 Recommendations and Guidelines 38References 383 Imbalanced Datasets: From Sampling to Classifiers 43T. Ryan Hoens and Nitesh V. Chawla3.1 Introduction 433.2 Sampling Methods 443.3 Skew-Insensitive Classifiers for Class Imbalance 493.4 Evaluation Metrics 523.5 Discussion 56References 574 Ensemble Methods for Class Imbalance Learning 61Xu-Ying Liu and Zhi-Hua Zhou4.1 Introduction 614.2 Ensemble Methods 624.3 Ensemble Methods for Class Imbalance Learning 664.4 Empirical Study 734.5 Concluding Remarks 79References 805 Class Imbalance Learning Methods for Support Vector Machines 83Rukshan Batuwita and Vasile Palade5.1 Introduction 835.2 Introduction to Support Vector Machines 845.3 SVMs and Class Imbalance 865.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods 875.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods 885.6 Summary 96References 966 Class Imbalance and Active Learning 101Josh Attenberg and Seyda Ertekin6.1 Introduction 1026.2 Active Learning for Imbalanced Problems 1036.3 Active Learning for Imbalanced Data Classification 1106.4 Adaptive Resampling with Active Learning 1226.5 Difficulties with Extreme Class Imbalance 1296.6 Dealing with Disjunctive Classes 1306.7 Starting Cold 1326.8 Alternatives to Active Learning for Imbalanced Problems 1336.9 Conclusion 144References 1457 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151Sheng Chen and Haibo He7.1 Introduction 1527.2 Preliminaries 1547.3 Algorithms 1577.4 Simulation 1677.5 Conclusion 1827.6 Acknowledgments 183References 1848 Assessment Metrics for Imbalanced Learning 187Nathalie Japkowicz8.1 Introduction 1878.2 A Review of Evaluation Metric Families and their Applicability to the Class Imbalance Problem 1898.3 Threshold Metrics: Multiple- Versus Single-Class Focus 1908.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration 1968.5 Conclusion 2048.6 Acknowledgments 205References 205Index 207

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.

“This book certainly qualifies as a reference for graduate studies in machine learning. Research students are sure to find it highly valuable and a prized possession, especially taking into account the wealth of supporting literature that the authors have brought to the fore.”  (Computing Reviews, 27 March 2014)

• Preface ixContributors xi1 Introduction 1Haibo He1.1 Problem Formulation 11.2 State-of-the-Art Research 31.3 Looking Ahead: Challenges and Opportunities 61.4 Acknowledgments 7References 82 Foundations of Imbalanced Learning 13Gary M. Weiss2.1 Introduction 142.2 Background 142.3 Foundational Issues 192.4 Methods for Addressing Imbalanced Data 262.5 Mapping Foundational Issues to Solutions 352.6 Misconceptions About Sampling Methods 362.7 Recommendations and Guidelines 38References 383 Imbalanced Datasets: From Sampling to Classifiers 43T. Ryan Hoens and Nitesh V. Chawla3.1 Introduction 433.2 Sampling Methods 443.3 Skew-Insensitive Classifiers for Class Imbalance 493.4 Evaluation Metrics 523.5 Discussion 56References 574 Ensemble Methods for Class Imbalance Learning 61Xu-Ying Liu and Zhi-Hua Zhou4.1 Introduction 614.2 Ensemble Methods 624.3 Ensemble Methods for Class Imbalance Learning 664.4 Empirical Study 734.5 Concluding Remarks 79References 805 Class Imbalance Learning Methods for Support Vector Machines 83Rukshan Batuwita and Vasile Palade5.1 Introduction 835.2 Introduction to Support Vector Machines 845.3 SVMs and Class Imbalance 865.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods 875.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods 885.6 Summary 96References 966 Class Imbalance and Active Learning 101Josh Attenberg and Seyda Ertekin6.1 Introduction 1026.2 Active Learning for Imbalanced Problems 1036.3 Active Learning for Imbalanced Data Classification 1106.4 Adaptive Resampling with Active Learning 1226.5 Difficulties with Extreme Class Imbalance 1296.6 Dealing with Disjunctive Classes 1306.7 Starting Cold 1326.8 Alternatives to Active Learning for Imbalanced Problems 1336.9 Conclusion 144References 1457 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151Sheng Chen and Haibo He7.1 Introduction 1527.2 Preliminaries 1547.3 Algorithms 1577.4 Simulation 1677.5 Conclusion 1827.6 Acknowledgments 183References 1848 Assessment Metrics for Imbalanced Learning 187Nathalie Japkowicz8.1 Introduction 1878.2 A Review of Evaluation Metric Families and their Applicability to the Class Imbalance Problem 1898.3 Threshold Metrics: Multiple- Versus Single-Class Focus 1908.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration 1968.5 Conclusion 2048.6 Acknowledgments 205References 205Index 207

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.

“This book certainly qualifies as a reference for graduate studies in machine learning. Research students are sure to find it highly valuable and a prized possession, especially taking into account the wealth of supporting literature that the authors have brought to the fore.”  (Computing Reviews, 27 March 2014)

• Preface ixContributors xi1 Introduction 1Haibo He1.1 Problem Formulation 11.2 State-of-the-Art Research 31.3 Looking Ahead: Challenges and Opportunities 61.4 Acknowledgments 7References 82 Foundations of Imbalanced Learning 13Gary M. Weiss2.1 Introduction 142.2 Background 142.3 Foundational Issues 192.4 Methods for Addressing Imbalanced Data 262.5 Mapping Foundational Issues to Solutions 352.6 Misconceptions About Sampling Methods 362.7 Recommendations and Guidelines 38References 383 Imbalanced Datasets: From Sampling to Classifiers 43T. Ryan Hoens and Nitesh V. Chawla3.1 Introduction 433.2 Sampling Methods 443.3 Skew-Insensitive Classifiers for Class Imbalance 493.4 Evaluation Metrics 523.5 Discussion 56References 574 Ensemble Methods for Class Imbalance Learning 61Xu-Ying Liu and Zhi-Hua Zhou4.1 Introduction 614.2 Ensemble Methods 624.3 Ensemble Methods for Class Imbalance Learning 664.4 Empirical Study 734.5 Concluding Remarks 79References 805 Class Imbalance Learning Methods for Support Vector Machines 83Rukshan Batuwita and Vasile Palade5.1 Introduction 835.2 Introduction to Support Vector Machines 845.3 SVMs and Class Imbalance 865.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods 875.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods 885.6 Summary 96References 966 Class Imbalance and Active Learning 101Josh Attenberg and Seyda Ertekin6.1 Introduction 1026.2 Active Learning for Imbalanced Problems 1036.3 Active Learning for Imbalanced Data Classification 1106.4 Adaptive Resampling with Active Learning 1226.5 Difficulties with Extreme Class Imbalance 1296.6 Dealing with Disjunctive Classes 1306.7 Starting Cold 1326.8 Alternatives to Active Learning for Imbalanced Problems 1336.9 Conclusion 144References 1457 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151Sheng Chen and Haibo He7.1 Introduction 1527.2 Preliminaries 1547.3 Algorithms 1577.4 Simulation 1677.5 Conclusion 1827.6 Acknowledgments 183References 1848 Assessment Metrics for Imbalanced Learning 187Nathalie Japkowicz8.1 Introduction 1878.2 A Review of Evaluation Metric Families and their Applicability to the Class Imbalance Problem 1898.3 Threshold Metrics: Multiple- Versus Single-Class Focus 1908.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration 1968.5 Conclusion 2048.6 Acknowledgments 205References 205Index 207