An efficient evolutionary feature selection algorithm with divide-and-conquer strategy for classification

As an important data preprocessing technique, feature selection aims to identify useful features and therefore reduce the dimensionality of the data. Recent studies have witnessed that evolutionary computation methods show significant potential in solving feature selection tasks. However, existing methods still encounter challenges due to the high computational costs, particularly when handling high-dimensional datasets. To tackle these issues, this work proposes a new evolutionary feature selection method with a divide-and-conquer strategy. The proposed method transforms a high-dimensional feature selection task into multiple low-dimensional sub-tasks. Multiple sub-populations corresponding to the multiple sub-tasks are evolved simultaneously. To further improve the quality of the generated candidate feature subsets, a set-based population update mechanism is introduced. Furthermore, diverse collaborative coevolution strategies are systematically explored and analyzed. The experiments conducted on 12 real-world classification datasets demonstrate that the proposed method achieves superior performance compared to 9 state-of-the-art feature selection methods. The results reveal that the proposed method is able to select smaller feature subsets while achieving higher classification accuracy across the majority of the used datasets with a reasonable low training time.