Feature selection with transductive support vector machines

Abstract

SVM-related feature selection has shown to be effective, while feature selection with transductive SVMs has been less studied. This paper investigates the use of transductive SVMs for feature selection, based on three SVM-relatcd feature selection methods: filtering scores + SVM wrapper, recursive feature elimination (RFE) and multiplicative updates(MU). We show transductive SVMs can be tailored to feature selection by embracing feature scores for feature filtering, or acting as wrappers and embedded feature selectors. We conduct experiments on the feature selection competition tasks to demonstrate the performance of Transductive SVMs in feature selection and classification. © Springer-Verlag Berlin Heidelberg 2006.