Using support vector regression for classification

Abstract

In this paper, a new method to solve the classified problems by using Support Vector Regression is introduced. Proposed method is called as SVR-C for short. In the method, through reconstructing the training set, each class through reconstructing the training set, each class value corresponding to a new training set, then use the SVR algorithm to train it and get a constructed model. And then, to a new instance, use the constructed model to train it and approximate the target class to the maximization of output value. Compared with M5P-C, SMO, J48, the effectiveness of our approach is tested on 16 publicly available datasets downloaded from the UCI. Comprehensive experiments are performed, and the results show that the SVR-C outperforms M5P-C and J48, and takes on comparative performance to SMO but has low standard-deviation. Moreover, our approach performs well on multi-class problems. © 2008 Springer-Verlag Berlin Heidelberg.