Tree-Structured Support Vector Machines for Multi-class Classification

Siyu Xia, Jiuxian Li, Liangzheng Xia, Chunhua Ju

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 k class problem, this method only needs k-1 SVM classifiers in the training phase, while it has less than k binary 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.