Feature selection based on regularization of sparsity based regression models by hesitant fuzzy correlation

Abstract

Abstract In this paper, the Ridge, LASSO and Elastic Net regression methods are adapted for the task of selecting feature. In order to enhance the feature selection performance via these methods, a Hesitant Fuzzy Correlation Matrix (HFCM) is added to the objective functions of these models for addressing the minimum redundancy of features. To this end, the fuzzy C-means clustering is utilized, and the obtained fuzzy clusters are projected on the features in a way that the number of fuzzy Membership Functions (MF) for each feature is equal to the number of clusters. Then, the projected MFs on each feature are considered as a Hesitant Fuzzy Set (HFS), and thereby the hesitant fuzzy correlation between features is calculated. Afterward, the obtained HFCM is employed in the regression methods for securing the minimum redundancy of features. Eventually, the accuracies of the selected features, achieved by these methods, are determined by three different classification models such as Naive Bayes, SVM and Decision Tree. A large number of experiments are conducted over twenty-four classification datasets to demonstrate the efficiency and applicability of using HFCM in some classical regression methods.