Proposing a novel feature selection algorithm based on Hesitant Fuzzy Sets and correlation concepts

In this paper, a Feature Selection (FS) method based on Hesitant Fuzzy Sets (HFS) is proposed. The ranking value of three filter methods (i.e. Fisher, Relief, Information Gain) for each feature are considered as Hesitant Fuzzy Elements (HFE) of that feature with respect to class relevancy, then hesitant correlation matrix of features is calculated. After that three similarity measures are considered to evaluate the second hesitant correlation matrix of features. The first correlation matrix represents the correlation of features with respect to their relevancy to the class. The second correlation matrix presents the correlation based on redundancy of features among themselves. One Hesitant Fuzzy Sets Clustering Algorithm (HFSCA) is run on these matrixes. Finally the intersection of clusters is considerd as a features subset which contains the highly relevance and lowly redundant features. The experimental results confirm the ability of our proposed method in both number of selected features and accuracy comparing to the other ones.