Support Vector Machines with Huffman Tree Architecture for Multiclass Classification

This paper proposes a novel multiclass support vector machine with Huffman tree architecture to quicken decision-making speed in pattern recognition. Huffman tree is an optimal binary tree, so the introduced architecture can minimize the number of support vector machines for binary decisions. Performances of the introduced approach are compared with those of the existing 6 multiclass classification methods using U.S. Postal Service Database and an application example of radar emitter signal recognition. The 6 methods includes one-against-one, one-against-all, bottom-up binary tree, two types of binary trees and directed acyclic graph. Experimental results show that the proposed approach is superior to the 6 methods in recognition speed greatly instead of decreasing classification performance.