A Hybrid Filter-Based Feature Selection Method via Hesitant Fuzzy and Rough Sets Concepts

Abstract

High dimensional microarray datasets are difficult to classify since they have many features with small number ofinstances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improvethe classification performance of microarray datasets by selecting the significant features. Combining the concepts ofrough sets, weighted rough set, fuzzy rough set and hesitant fuzzy sets for developing an effective algorithm is the maincontribution of this paper. The mentioned method has two steps, in the first step, four discretization approaches areapplied to discretize continuous datasets and selects a primary subset of features by combining of weighted rough setdependency degree and information gain via hesitant fuzzy aggregation approach. In the second step, a significancemeasure of features (defined by fuzzy rough concepts) is employed to remove redundant features from primary set.The Wilcoxon Signed Ranked tes (A Non-parametric statistical test) is conducted for comparing the presented methodwith ten feature selection methods across seven datasets. The results of experiments show that the proposed methodis able to select a significant subset of features and it is an effective method in the literature in terms of classificationperformance and simplicity.