A new way for combining filter feature selection methods

Abstract

This study investigates the issue of obtaining stable ranking from the fusion of the result of multiple filtering methods. Rank aggregation is the process of performing multiple runs of feature selection and then aggregating the results into a final ranked list. However, a fundamental question of is how to aggregate the individual results into a single robust ranked feature list. There are a number of available methods, ranging from simple to complex. Hence we present a new rank aggregation approach. The proposed approach is composed of two stages: in the first we evaluate he similarity and stability of single filtering methods then, in the second we aggregate the results of the stable ones. The obtained results on the Australian and German credit datasets using support vector machine and decision tree confirms that ensemble feature ranking have a major impact in the performance improvement.