Imbalanced Learning (eBook, PDF)

Foundations, Algorithms, and Applications
Redaktion: He, Haibo; Ma, Yunqian

• Format: PDF

Produktbeschreibung

The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: * Foundations of Imbalanced Learning * Imbalanced Datasets: From Sampling to Classifiers * Ensemble Methods for Class Imbalance Learning * Class Imbalance Learning Methods for Support Vector Machines * Class Imbalance and Active Learning * Nonstationary Stream Data Learning with Imbalanced Class Distribution * Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers 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 directions.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 216
• Erscheinungstermin: 23. Mai 2013
• Englisch
• ISBN-13: 9781118646205
• Artikelnr.: 39054480

Autorenporträt

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.

Inhaltsangabe

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

Rezensionen

"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)