A self-adaptive multi-objective feature selection approach for classification problems

Xue, Yu; Zhu, Haokai; Neri, Ferrante

Abstract

In classification tasks, feature selection (FS) can reduce the data dimensionality and may also improve classification accuracy, both of which are commonly treated as the two objectives in FS problems. Many meta-heuristic algorithms have been applied to solve the FS problems and they perform satisfactorily when the problem is relatively simple. However, once the dimensionality of the datasets grows, their performance drops dramatically. This paper proposes a self-adaptive multi-objective genetic algorithm (SaMOGA) for FS, which is designed to maintain a high performance even when the dimensionality of the datasets grows. The main concept of SaMOGA lies in the dynamic selection of five different crossover operators in different evolution process by applying a self-adaptive mechanism. Meanwhile, a search stagnation detection mechanism is also proposed to prevent premature convergence. In the experiments, we compare SaMOGA with five multi-objective FS algorithms on sixteen datasets. According to the experimental results, SaMOGA yields a set of well converged and well distributed solutions on most data sets, indicating that SaMOGA can guarantee classification performance while removing many features, and the advantage over its counterparts is more obvious when the dimensionality of datasets grows.

Citation

Xue, Y., Zhu, H., & Neri, F. (2022). A self-adaptive multi-objective feature selection approach for classification problems. Integrated Computer-Aided Engineering, 29(1), 3-21. https://doi.org/10.3233/ICA-210664

• Journal Article Type: Article
• Acceptance Date: Jul 27, 2021
• Online Publication Date: Dec 28, 2021
• Publication Date: 2022
• Deposit Date: Jul 27, 2021
• Publicly Available Date: Dec 28, 2021
• Journal: Integrated Computer-Aided Engineering
• Print ISSN: 1069-2509
• Electronic ISSN: 1875-8835
• Publisher: IOS Press
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Issue: 1
• Pages: 3-21
• DOI: https://doi.org/10.3233/ICA-210664
• Keywords: Feature selection; self-adaptive; multi-objective genetic algorithm; stagnation detection; classification
• Public URL: https://nottingham-repository.worktribe.com/output/5842556
• Publisher URL: https://content.iospress.com/articles/integrated-computer-aided-engineering/ica210664
• Additional Information: The final publication is available at IOS Press through https://content.iospress.com/articles/integrated-computer-aided-engineering/ica210664