Letters: Adaptive binary tree for fast SVM multiclass classification

Authors:
Jin Chen;Cheng Wang;Runsheng Wang
Affiliations:
ATR Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China;ATR Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China;ATR Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha, China
Venue:
Neurocomputing
Year:
2009

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Abstract

This paper presents an adaptive binary tree (ABT) to reduce the test computational complexity of multiclass support vector machine (SVM). It achieves a fast classification by: (1) reducing the number of binary SVMs for one classification by using separating planes of some binary SVMs to discriminate other binary problems; (2) selecting the binary SVMs with the fewest average number of support vectors (SVs). The average number of SVs is proposed to denote the computational complexity to exclude one class. Compared with five well-known methods, experiments on many benchmark data sets demonstrate our method can speed up the test phase while remain the high accuracy of SVMs.