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An adaptive ranking moth flame optimizer for feature selection

Author

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  • Yu, Xiaobing
  • Wang, Haoyu
  • Lu, Yangchen

Abstract

Feature selection is to identify informative and concise sub-features from raw datasets, which can be modelled as an optimization issue. An adaptive ranking moth-flame optimization (ARMFO) is developed to solve the problem. The proposed ARMFO algorithm has five improvements: the ranking probability divides moths into better and worse groups; each group performs appropriate position-update equations to enhance the local and global search; a self-adaptive chaotic mutation is used to increase the quality of the best flame; a greedy selection is to maintain better solutions, and the structure of flames is changed. The search ability of the ARMFO algorithm is verified on a test suit, and the algorithm has obtained the best results on twenty-one functions, which accounts for 72.41%. Then, the proposed ARMFO algorithm and seven swarm intelligent algorithms are used for feature selection on fourteen datasets from UCI. The proposed ARMFO algorithm has obtained satisfactory results on 9 datasets compared to its seven rivals.

Suggested Citation

  • Yu, Xiaobing & Wang, Haoyu & Lu, Yangchen, 2024. "An adaptive ranking moth flame optimizer for feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 164-184.
    • Handle: RePEc:eee:matcom:v:219:y:2024:i:c:p:164-184
    DOI: 10.1016/j.matcom.2023.12.022
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