Ensemble of feature selection methods: A hesitant fuzzy sets approach

Abstract

Recently, there has been a great attention to develop feature selection methods on the microarray high dimensional datasets. In this paper, an innovative method based on Maximum Relevancy and Minimum Redundancy (MRMR) approach by using Hesitant Fuzzy Sets (HFSs) is proposed to deal with feature subset selection; the method is called MRMR-HFS. MRMR-HFS is a novel filter-based feature selection algorithm that selects features by ensemble of ranking algorithms (as the measure of feature-class relevancy that must be maximized) and similarity measures (as the measure of feature-feature redundancy that must be minimized). The combination of ranking algorithms and similarity measures are done by using the fundamental concepts of information energies of HFSs. The proposed method has been inspired from Correlation based Feature Selection (CFS) within the sequential forward search in order to present a robust feature selection tool to solve high dimensional problems. To evaluate the effectiveness of the MRMR-HFS, several experimental results are carried out on nine well-known microarray high dimensional datasets. The obtained results are compared with those of other similar state-of-the-art algorithms including Correlation-based Feature Selection (CFS), Fast Correlation-based Filter (FCBF), Intract (INT), and Maximum Relevancy Minimum Redundancy (MRMR). The outcomes of comparison carried out via some non-parametric statistical tests confirm that the MRMR-HFS is effective for feature subset selection in high dimensional datasets in terms of accuracy, sensitivity, specificity, G-mean, and number of selected features.