A hybrid filter-based feature selection method via hesitant fuzzy and rough sets concepts

Document Type: Original Manuscript


1 Department of computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

2 Department of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran


High dimensional microarray datasets are difficult to classify since they have many features with small number of
instances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improve
the classification performance of microarray datasets by selecting the significant features. Combining the concepts of
rough sets, weighted rough set, fuzzy rough set and hesitant fuzzy sets for developing an effective algorithm is the main
contribution of this paper. The mentioned method has two steps, in the first step, four discretization approaches are
applied to discretize continuous datasets and selects a primary subset of features by combining of weighted rough set
dependency degree and information gain via hesitant fuzzy aggregation approach. In the second step, a significance
measure 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 method
with ten feature selection methods across seven datasets. The results of experiments show that the proposed method
is able to select a significant subset of features and it is an effective method in the literature in terms of classification
performance and simplicity.


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