Imbalanced learning : foundations, algorithms, and applications

Imbalanced learning foundations, algorithms, and applications

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...

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Detalles Bibliográficos
Otros Autores: He, Haibo, 1976- (-), Ma, Yunqian
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, NJ : Wiley c2013.
Materias:
Data mining.
Information resources management.
Information resources > Evaluation.
System analysis > Mathematical models.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009665113706719
Tabla de Contenidos:
  • 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.

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