ABSTRACT
The main challenge of classification systems is the processing of undesirable data. Filter-based feature selection is an effective solution to improve the performance of classification systems by selecting the significant features and discarding the undesirable ones. The success of this solution depends on the extracted information from data characteristics. For this reason, many research theories have been introduced to extract different feature relations. Unfortunately, traditional feature selection methods estimate the feature significance based on either individually or dependency discriminative ability. This paper introduces a new ensemble feature selection, called fuzzy feature selection based on relevancy, redundancy, and dependency (FFS-RRD). The proposed method considers both individually and dependency discriminative ability to extract all possible feature relations. To evaluate the proposed method, experimental comparisons are conducted with eight state-of-the-art and conventional feature selection methods. Based on 13 benchmark datasets, the experimental results over four well-known classifiers show the outperformance of our proposed method in terms of classification performance and stability.
