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<p>As data inflation is inevitable with the deep development of different fields, feature selection is becoming an indispensable process in data processing. Feature selection has attracted much attention from researchers for processing complex data faster with less cost and enhancing data interpretability. In the last decade, multi-objective feature selection (MOFS) research has shown a diversified development, but not yet a large span of systematic summarization and analysis of MOFS research related to metaheuristic algorithms in the last decade. For this reason, this paper investigates recent metaheuristic algorithm MOFS-related researches (2013 ~ 2023), statistically analyzes and discusses relevant popular methods and their applications. After reviewing the above studies, a summary of commonly used datasets and open-source frameworks for metaheuristic algorithms in MOFS studies is presented, and we further discuss the open issues and potential challenges faced by metaheuristic algorithms in MOFS research.</p>

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Recent metaheuristic algorithms for multi-objective feature selection: review, applications, open issues and challenges

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Haibin Ouyang,
  • Xuyu Lin,
  • Steven Li,
  • Liqun Gao,
  • Essam H. Houssein

摘要

As data inflation is inevitable with the deep development of different fields, feature selection is becoming an indispensable process in data processing. Feature selection has attracted much attention from researchers for processing complex data faster with less cost and enhancing data interpretability. In the last decade, multi-objective feature selection (MOFS) research has shown a diversified development, but not yet a large span of systematic summarization and analysis of MOFS research related to metaheuristic algorithms in the last decade. For this reason, this paper investigates recent metaheuristic algorithm MOFS-related researches (2013 ~ 2023), statistically analyzes and discusses relevant popular methods and their applications. After reviewing the above studies, a summary of commonly used datasets and open-source frameworks for metaheuristic algorithms in MOFS studies is presented, and we further discuss the open issues and potential challenges faced by metaheuristic algorithms in MOFS research.