A two-way accelerator for feature selection using a monotonic fuzzy conditional entropy

Fuzzy rough set is a highly effective mathematical method for feature selection, which offers clear interpretability without expert knowledge. However, most of fuzzy -rough feature selection methods are to rely on all samples and candidate features during the selection of a best feature at each iteration. This often shares high computation complexity and is inefficient for large datasets. Therefore, a two-way accelerator for feature selection is presented by stepwise narrowing the search space for both samples and features. As the foundation for our accelerator, a monotonic fuzzy conditional entropy, named parameterized fuzzy granule -based conditional entropy, is first proposed to guide the feature selection process. After identifying a best feature, a sample -based accelerator is then designed to disregard redundant samples for the calculation of the newly defined entropy. A feature -based accelerator is further proposed to eliminate redundant candidate features, of which the inclusion cannot change the proposed entropy of a currently selected feature subset. Our accelerator is finally developed by integrating the sample -based accelerator with the feature -based accelerator. Experimental comparisons demonstrate the effectiveness and efficiency of the proposed two-way accelerator for feature selection.