A Parallel Multi-Class Classification Support Vector Machine Based on Sequential Minimal Optimization

Abstract

Support vector machine (SVM) is originally developed for binary classification problems. In order to solve practical multi-class problems, various approaches such as one-against-rest (1-a-r), one-against-one (1-a-1) and decision trees based SVM have been presented. The disadvantages of the existing methods of SVM multi-class classification are analyzed and compared in this paper, such as 1-a-r is difficult to train and the classifying speed of 1-a-1 is slow. To solve these problems, a parallel multi-class SVM based on sequential minimal optimization (SMO) is proposed in this paper. This method combines SMO, parallel technology, DTSVM and cluster. Experiments have been made on University of California-Irvine (UCI) database, in which five benchmark datasets have been selected for testing. The experiments are executed to compare 1-a-r, 1-a-1 and this method on training and testing time. The result shows that the speeds of training and classifying are improved remarkably