SVMTOCP: A binary tree base SVM approach through optimal multi-class binarization

Abstract

The tree architecture has been employed to solve multi-class problems based on SVM. It is an alternative to the well known OVO/OVA strategies. Most of the tree base SVM classifiers try to split the multi-class space, mostly, by some clustering like algorithms into several binary partitions. One of the main drawbacks of this is that the natural class structure is not taken into account. Also the same SVM parameterization is used for all classifiers. Here a preliminary and promising result of a multi-class space partition method that account for data base class structure and allow node's parameter specific solutions is presented. In each node the space is split into two class problem possibilities and the best SVM solution found. Preliminary results show that accuracy is improved, lesser information is required, each node reaches specific cost values and hard separable classes can easily be identified. © 2012 Springer-Verlag.