Multi-class cluster support vector machines

Abstract

Based on the idea of nonparallel hyperplanes,a novel multi-class cluster support vector machine method was proposed to settle the multi-class classification problem of support vector machines.For a k-class classification problem,it trained k-hyperplanes respectively,and each one lay as close as possible to self-class while being apart from the rest classes as far as possible.Then,labels of new samples were determined by the class of their nearest hyperplane,thus the inherent limitations of One-Against-One(OAO) and One-Against-All(OAA) methods can be avoided,such as "decision blind-area" and "unbalanced classes".Finally,experiments on UCI and HCL2000 datasets show that the proposed method significantly outperforms traditional OAO and OAA in terms of recognition accuracy.