Feature subset selection using multimodal multiobjective differential evolution

The main aim of feature subset selection is to find the minimum number of required features to perform classification without affecting the accuracy. It is one of the useful real-world applications for different types of classification datasets. Different feature subsets may achieve similar classification accuracy, which can help the user to select the optimal features. There are two main objectives involved in selecting a feature subset: minimizing the number of features and maximizing the accuracy. However, most of the existing studies do not consider multiple feature subsets of the same size. In this paper, we have proposed an algorithm for multimodal multiobjective optimization based on differential evolution with respect to the feature subset selection problem. We have proposed the probability initialization method to identify the selected features with equal distribution in the search space. We have also proposed a niching technique to explore the search space and exploit the nearby solutions. Further, we have proposed a convergence archive to locate and store the optimal feature subsets. Exhaustive experimentation has been conducted on different datasets with varying characteristics to identify multiple feature subsets. We have also proposed an evaluation metric for the quantitative comparison of the proposed algorithm with the existing algorithms. Results have also been compared with existing algorithms in the objective space and in terms of classification accuracy, which shows the effectiveness of the proposed algorithm.