Grouping-Based Crowding Differential Evolution Approaches for Multimodal Feature Selection

Abstract

Feature selection can increase the classification accuracy and reduce the scale of feature subset, which is important in various machine learning tasks. However, there are various preferences and limitations for the usage of features in different application scenes, and thus different scenes may require different feature subsets. To this end, multimodal feature selection, which aims to simultaneously find multiple feature subsets with low overlap and promising classification accuracy, is also important but does not attract enough attention yet. Therefore, a new multimodal feature selection model is formulated and two grouping-based crowding differential evolution approaches are proposed in this paper. Mutual information is utilized to cluster features with high correlation and the two proposed grouping-based crowding differential evolution approaches incorporate a shuffle-based grouping strategy and a threshold-based grouping strategy, respectively, so as to simultaneously search for multiple low-overlap feature subsets with promising classification accuracy. Experimental results on eight widely used datasets validate the effectiveness of the proposed approaches.