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Feature selection makes machine learning models less complex, more accurate, and more interpretable. The Slime Mould Algorithm (SMA) is a bio-inspired technique that leverages the demeanour of slime moulds to solve optimisation problems efficiently. This survey paper aims to deliver an extensive review of the recent research works involving the application of SMA as well as SMA fused with other optimisation methods to develop a solution for the feature selection problems. This involves summarizing and categorizing relevant research papers, methodologies, and findings. The survey paper consolidates and structures the existing knowledge about how the SMA is applied in the context of feature selection. Our work will help researchers and practitioners better comprehend the current advancement in this field.

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State-of-the-Art in Feature Selection: Applications of the Slime Mould Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Taniya Chatterjee,
  • Puja Bhakta,
  • Mili Ghosh,
  • Debaditya Barman

摘要

Feature selection makes machine learning models less complex, more accurate, and more interpretable. The Slime Mould Algorithm (SMA) is a bio-inspired technique that leverages the demeanour of slime moulds to solve optimisation problems efficiently. This survey paper aims to deliver an extensive review of the recent research works involving the application of SMA as well as SMA fused with other optimisation methods to develop a solution for the feature selection problems. This involves summarizing and categorizing relevant research papers, methodologies, and findings. The survey paper consolidates and structures the existing knowledge about how the SMA is applied in the context of feature selection. Our work will help researchers and practitioners better comprehend the current advancement in this field.