A decision based one-against-one method for multi-class support vector machine

Authors:
R. Debnath;N. Takahide;H. Takahashi
Affiliations:
The University of Electro-Communications, Department of Information and Communication Engineering, 1-5-1 Chofugaoka, Chofu-shi, 182-8585, Tokyo, Japan;The University of Electro-Communications, Department of Information and Communication Engineering, 1-5-1 Chofugaoka, Chofu-shi, 182-8585, Tokyo, Japan;The University of Electro-Communications, Department of Information and Communication Engineering, 1-5-1 Chofugaoka, Chofu-shi, 182-8585, Tokyo, Japan
Venue:
Pattern Analysis & Applications
Year:
2004

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Abstract

The support vector machine (SVM) has a high generalisation ability to solve binary classification problems, but its extension to multi-class problems is still an ongoing research issue. Among the existing multi-class SVM methods, the one-against-one method is one of the most suitable methods for practical use. This paper presents a new multi-class SVM method that can reduce the number of hyperplanes of the one-against-one method and thus it returns fewer support vectors. The proposed algorithm works as follows. While producing the boundary of a class, no more hyperplanes are constructed if the discriminating hyperplanes of neighbouring classes happen to separate the rest of the classes. We present a large number of experiments that show that the training time of the proposed method is the least among the existing multi-class SVM methods. The experimental results also show that the testing time of the proposed method is less than that of the one-against-one method because of the reduction of hyperplanes and support vectors. The proposed method can resolve unclassifiable regions and alleviate the over-fitting problem in a much better way than the one-against-one method by reducing the number of hyperplanes. We also present a direct acyclic graph SVM (DAGSVM) based testing methodology that improves the testing time of the DAGSVM method.