Binary tree support vector machine based on kernel fisher discriminant for multi-classification

Authors:
Bo Liu;Xiaowei Yang;Zhifeng Hao
Affiliations:
College of Computer Science and Engineering, South China University of Technology, Guangzhou, P.R. China;School of Mathematical Science, South China University of Technology, Guangzhou, P.R. China;School of Mathematical Science, South China University of Technology, Guangzhou, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

In order to improve the accuracy of the conventional algorithms for multi-classifications, we propose a binary tree support vector machine based on Kernel Fisher Discriminant in this paper. To examine the training accuracy and the generalization performance of the proposed algorithm, One-against-All, One-against-One and the proposed algorithms are applied to five UCI data sets. The experimental results show that in general, the training and the testing accuracy of the proposed algorithm is the best one, and there exist no unclassifiable regions in the proposed algorithm.