Parameter optimization of kernel-based one-class classifier on imbalance learning

Zhuang, Ling and Dai, Honghua 2006, Parameter optimization of kernel-based one-class classifier on imbalance learning, Journal of computers, vol. 1, no. 7, pp. 32-40.

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Title Parameter optimization of kernel-based one-class classifier on imbalance learning
Author(s) Zhuang, Ling
Dai, HonghuaORCID iD for Dai, Honghua
Journal name Journal of computers
Volume number 1
Issue number 7
Start page 32
End page 40
Publisher Academy Publisher
Place of publication Oulu, Finland
Publication date 2006-10
ISSN 1796-203X
Keyword(s) one-class classification framework
Support Vector Data Description(SVDD)
one-class Support Vector Machine
imbalance learning
Summary Compared with conventional two-class learning schemes, one-class classification simply uses a single class in the classifier training phase. Applying one-class classification to learn from unbalanced data set is regarded as the recognition based learning and has shown to have the potential of achieving better performance. Similar to twoclass learning, parameter selection is a significant issue, especially when the classifier is sensitive to the parameters. For one-class learning scheme with the kernel function, such as one-class Support Vector Machine and Support Vector Data Description, besides the parameters involved in the kernel, there is another one-class specific parameter: the rejection rate v. In this paper, we proposed a general framework to involve the majority class in solving the parameter selection problem. In this framework, we first use the minority target class for training in the one-class classification stage; then we use both minority and majority class for estimating the generalization performance of the constructed classifier. This generalization performance is set as the optimization criteria. We employed the Grid search and Experiment Design search to attain various parameter settings. Experiments on UCI and Reuters text data show that the parameter optimized one-class classifiers outperform all the standard one-class learning schemes we examined.
Language eng
Field of Research 080107 Natural Language Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2006, Academy Publisher
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Document type: Journal Article
Collection: School of Engineering and Information Technology
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