Multi-population differential evolution approach for feature selection with mutual information ranking

• Published in: Expert systems with applications Vol. 260; p. 125404
• Main Authors: Yu, Fei, Guan, Jian, Wu, Hongrun, Wang, Hui, Ma, Biyang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2025
• Subjects: Differential evolution (DE); Evolutionary computation (EC); Feature selection (FS); Mutual information (MI); Opposition-based learning (OBL); Opposition-based learning (OBL); Evolutionary computation (EC); Feature selection (FS); Mutual information (MI); Differential evolution (DE)
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125404

Feature selection is a crucial aspect of data preprocessing because of the significant effect of redundant features on classification performance and the extensive computational resources required. Evolutionary algorithm-based feature-selection methods have shown remarkable results in determining the optimal feature subset. To further enhance classification performance, this paper proposes a novel multi-population differential evolution approach for feature selection with mutual information ranking (MI-MPODE). Firstly, population preprocessing guided by mutual information is employed to reduce the dimensionality of the initial feature space. Then, the feature subset obtained from mutual information serves as the initial population for MI-MPODE. MI-MPODE incorporates a novel multi-population information-sharing mechanism, with common individuals from three layers contributing to inter-subpopulation information sharing. Additionally, an individual enhancement strategy is proposed to handle the variations of individuals in the population, and a Lens imaging opposition-based learning method is adopted to improve the algorithm’s optimization capability. MI-MPODE is compared with several state-of-the-art evolutionary algorithm-based feature selection methods through experimental comparisons. The results show that MI-MPODE outperforms all comparison algorithms on more than half of the datasets, with a significant reduction in the number of features used, demonstrating a significant advantage over competitors. •This paper proposes a novel multi-population information sharing mechanism.•MI technology to optimize the initial population.•The individual stratification method and the LensOBL strategy improve the algorithm performance.•The performance of MI-MPODE is better than competitors on many datasets.