Imbalanced Learning. Foundations, Algorithms, and Applications.pdf

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1、BrochureBrochure More information from http:/ Learning.Foundations,Algorithms,and Applications Description:The first book of its kind to review the current status and future direction of the exciting new branch ofmachine learning/data mining called imbalanced learningImbalanced learning focuses on h

2、ow an intelligent system can learn when it is provided with imbalanceddata.Solving imbalanced learning problems is critical in numerous dataintensive networked systems,including surveillance,security,Internet,finance,biomedical,defense,and more.Due to the inherentcomplex characteristics of imbalance

3、d data sets,learning from such data requires new understandings,principles,algorithms,and tools to transform vast amounts of raw data efficiently into information andknowledge representation.The first comprehensive look at this new branch of machine learning,this book offers a critical review of the

4、problem of imbalanced learning,covering the state of the art in techniques,principles,and realworldapplications.Featuring contributions from experts in both academia and industry,Imbalanced Learning:Foundations,Algorithms,and Applications provides chapter coverage on:-Foundations of Imbalanced Learn

5、ing-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

6、LearningImbalanced Learning:Foundations,Algorithms,and Applications will help scientists and engineers learn howto tackle the problem of learning from imbalanced datasets,and gain insight into current developments inthe field as well as future research directions.Contents:Preface ixContributors xi1

7、Introduction 1 Haibo He1.1 Problem Formulation,11.2 StateoftheArt Research,31.3 Looking Ahead:Challenges and Opportunities,61.4 Acknowledgments,7References,82 Foundations of Imbalanced Learning 13 Gary M.Weiss2.1 Introduction,142.2 Background,142.3 Foundational Issues,192.4 Methods for Addressing Im

8、balanced 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 43 T.Ryan Hoens and Nitesh V.Chawla3.1 Introduction,433.2 Sampling Methods,443.3 SkewInsensi

9、tive Classifiers for Class Imbalance,493.4 Evaluation Metrics,523.5 Discussion,56References,574 Ensemble Methods for Class Imbalance Learning 61 XuYing Liu and ZhiHua Zhou4.1 Introduction,614.2 Ensemble Methods,624.3 Ensemble Methods for Class Imbalance Learning,664.4 Empirical Study,734.5 Concludin

10、g Remarks,79References,805 Class Imbalance Learning Methods for Support Vector Machines 83 Rukshan 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

11、.5 Internal Imbalance Learning Methods for SVMs:Algorithmic Methods,885.6 Summary,96References,966 Class Imbalance and Active Learning 101 Josh Attenberg and Seyda Ertekin6.1 Introduction,1026.2 Active Learning for Imbalanced Problems,103 6.3 Active Learning for Imbalanced Data Classification,1106.4

12、 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 Imbalanc

13、ed Class Distribution 151 Sheng 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 187 Nathalie Japkowicz8.1 Introduction,1878.2 A Review of Evaluation Metri

14、c Families and their Applicabilityto the Class Imbalance Problem,1898.3 Threshold Metrics:Multiple Versus SingleClass Focus,1908.4 Ranking Methods and Metrics:Taking Uncertainty into Consideration,1968.5 Conclusion,2048.6 Acknowledgments,205References,205Index 207Ordering:Order Online-http:/ by Fax-

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