Local class boundaries for support vector machine

Authors:
Guihua Wen;Caihui Zhou;Jia Wei;Lijun Jiang
Affiliations:
South China University of Technology, Guangzhou, China;South China University of Technology, Guangzhou, China;South China University of Technology, Guangzhou, China;South China University of Technology, Guangzhou, China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
Year:
2010

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Abstract

The support vector machine (SVM) has proved effective in classification. However, SVM easily becomes intractable in its memory and time requirements to deal with the large data, and also can not nicely deal with noisy, sparse, and imbalanced data. To overcome these issues, this paper presents a new local support vector machine that first finds k nearest neighbors from each class respectively for the query sample and then SVM is trained locally on all these selected nearest neighbors to perform the classification. This approach is efficient, simple and easy to implement. The conducted experiments on challenging benchmark data sets validate the proposed approach in terms of classification accuracy and robustness.