Multi-Class classification using support vector machines in binary tree architecture

Abstract

This paper presents architecture of Support Vector Machine classifiers arranged in a binary tree structure for solving multi-class classification problems with increased efficiency. The proposed SVM based Binary Tree Architecture (SVM-BTA) takes advantage of both the efficient computation of the tree architecture and the high classification accuracy of SVMs. Clustering algorithm is used to convert the multi-class problem into binary tree, in which the binary decisions are made by the SVMs. The proposed clustering model utilizes distance measures at the kernel space, not at the input space. The performance of this method was measured on the problem of recognition of handwritten digits and letters using samples from MNIST, Pendigit, Optdigit and Statlog database of segmented digits and letters. The results of the experiments indicate that this method has much faster training and testing times than the widely used multi-class SVM methods like “one-against-one” and “one-against-all” while keeping comparable recognition rates. The experiments showed that this method becomes more favorable as the number of classes in the recognition problem increases.