On the Class Imbalance Problem

The class imbalance problem has been recognized in many practical domains and a hot topic of machine learning in recent years. In such a problem, almost all the examples are labeled as one class, while far fewer examples are labeled as the other class, usually the more important class. In this case,...

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Bibliographic Details
Published in2008 Fourth International Conference on Natural Computation Vol. 4; pp. 192 - 201
Main Authors Xinjian Guo, Yilong Yin, Cailing Dong, Gongping Yang, Guangtong Zhou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Biomedical monitoring
Computer science
Condition monitoring
Data mining
Diseases
Fault detection
Intrusion detection
Machine learning
Machine learning algorithms
Radar detection
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Summary:The class imbalance problem has been recognized in many practical domains and a hot topic of machine learning in recent years. In such a problem, almost all the examples are labeled as one class, while far fewer examples are labeled as the other class, usually the more important class. In this case, standard machine learning algorithms tend to be overwhelmed by the majority class and ignore the minority class since traditional classifiers seeking an accurate performance over a full range of instances. This paper reviewed academic activities special for the class imbalance problem firstly. Then investigated various remedies in four different levels according to learning phases. Following surveying evaluation metrics and some other related factors, this paper showed some future directions at last.
ISBN:9780769533049
0769533043
ISSN:2157-9555
DOI:10.1109/ICNC.2008.871