A binary linear predictive evolutionary algorithm with feature analysis for multiobjective feature selection in classification

Multiobjective feature selection (MOFS), which aims to obtain a set of Pareto optimal feature subsets by simultaneously maximizing classification accuracy and minimizing the number of selected features, has attracted considerable attention recently. However, most existing studies still face a challenge that locating more well-distributed Pareto optimal feature subsets, especially for high-dimensional complex datasets. In response to this challenge, this paper proposes a binary linear predictive evolutionary algorithm with feature analysis (MBLPE) for MOFS. In this paper, a feature analysis-based selection method is proposed to select effective solutions into the next generation. Concretely, two subset evaluation indicators are designed to efficiently deal with duplicated feature subsets during evolution. Then, a fitness allocation is constructed to select effective solutions, improving population diversity. Moreover, a fisher score-based initialization scheme is designed when handling high-dimensional complex datasets. The proposed scheme effectively removes irrelevant and redundant features in search space by identifying features with strong discriminant performance in advance, thereby reducing computational cost. In comparison with seven state-of-the-art algorithms on 18 classification datasets with different characteristics, the proposed MBLPE finds more diverse feature subsets with better convergence in solving the MOFS problems.

Authors

• Ting Zhou
• Limin Wang State Key Laboratory for Novel Software Technology, Nanjing University, China
• Xuming Han
• Zhiquan Liu
• Minghan Gao

Keywords

• Feature selection
• Classification
• Multiobjective optimization
• Linear predictive evolutionary algorithm