Kernel methods for mixed feature selection

Abstract. This paper introduces two feature selection methods to deal with heterogeneous data that include continuous and categorical variables. We propose to plug a dedicated kernel that handles both kind of variables into a Recursive Feature Elimination procedure using either a non-linear SVM or Multiple Kernel Learning. These methods are shown to offer significantly better predictive results than state-of-the-art alternatives on a variety of high-dimensional classification tasks.