Twi-Map support vector machine for multi-classification problems

Authors:
Zhifeng Hao;Bo Liu;Xiaowei Yang;Yanchun Liang;Feng Zhao
Affiliations:
Department of Applied Mathematics, South China University of Technology, Guangzhou, China;College of Computer Science and Engineering, South China University of Technology, Guangzhou, China;Department of Applied Mathematics, South China University of Technology, Guangzhou, China;College of Computer Science and Technology, Jilin University, Changchun, China;Department of Applied Mathematics, South China University of Technology, Guangzhou, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

In this paper, a novel method called Twi-Map Support Vector Machines (TMSVM) for multi-classification problems is presented. Our ideas are as follows: Firstly, the training data set is mapped into a high-dimensional feature space. Secondly, we calculate the distances between the training data points and hyperplanes. Thirdly, we view the new vector consisting of the distances as new training data point. Finally, we map the new training data points into another high-dimensional feature space with the same kernel function and construct the optimal hyperplanes. In order to examine the training accuracy and the generalization performance of the proposed algorithm, One-against-One algorithm, Fuzzy Least Square Support Vector Machine (FLS-SVM) and the proposed algorithm are applied to five UCI data sets. Comparison results obtained by using three algorithms are given. The results show that the training accuracy and the testing one of the proposed algorithm are higher than those of One-against-One and FLS-SVM.