Information theoretic feature selection for Weisfeiler-Lehman graph kernels

Abstract

Classification of structured data has gained importance recently. One important problem that exploits structured data is to computationally estimate some properties of small molecules. Among the algorithms for graph classification, kernel machines constitute a large portion. Although there are a number of graph kernels proposed in the literature, feature selection has only recently been considered in this domain. In this paper, we propose a feature selection method based on permutation tests, which not only improves the classification performance, but also provides space efficiency by eliminating uninformative features at the beginning. We demonstrate the performance of the method on a number of data sets in chemical compound classification.