Feature selection for multi-class problems using support vector machines

Abstract

Since feature selection can remove the irrelevant features and improve the performance of learning systems, it is an crucial step in machine learning. The feature selection methods using support vector machines have obtained satisfactory results, but the previous works are usually for binary classification, and needs auxiliary techniques to be extended to multiple classification. In this paper, we propose a prediction risk based feature selection method using multiple classification support vector machines. The performance of the proposed method is compared with the previous methods of optimal brain damage based feature selection methods using binary support vector machines. The results of experiments on UCI data sets show that prediction risk based feature selection method obtains better results than the previous methods using support vector machines for multiple classification problems. © Springer-Verlag Berlin Heidelberg 2004.