Tree-Structured Support Vector Machines for Multi-class Classification

Authors:
Siyu Xia;Jiuxian Li;Liangzheng Xia;Chunhua Ju
Affiliations:
School of Automation, Southeast University, Nanjing 210096, China;School of Automation, Southeast University, Nanjing 210096, China;School of Automation, Southeast University, Nanjing 210096, China;College of Computer Information and Engineering, Zhejiang Gongshang University, Hangzhou 310035, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

In this paper, a non-balanced binary tree is proposed for extending support vector machines (SVM) to multi-class problems. The non-balanced binary tree is constructed based on the prior distribution of samples, which can make the more separable classes separated at the upper node of the binary tree. For an kclass problem, this method only needs k-1 SVM classifiers in the training phase, while it has less than kbinary test when making a decision. Further, this method can avoid the unclassifiable regions that exist in the conventional SVMs. The experimental result indicates that maintaining comparable accuracy, this method is faster than other methods in classification.