D-FS: A Novel Integration Method of Discretization and Feature Selection

Discretization and feature selection are two basic preprocessing stages of data mining. However, it often results in information loss due to these two separate stages. This paper proposes a novel supervised multivariate discretizer integrated with feature selection, called D-FS. It takes into consideration of the interactions of both different cut-points and features, and achieves feature selection by discretization. D-FS can avoid the information loss caused by the independence of discretization and feature selection. Compared with several state-of-the-art discretizers, D-FS retains a smaller subset of both cut-points and features, while achieves competitive classification performance combined with different classifiers.