An adaptive ranking moth flame optimizer for feature selection

Xiaobing Yu, Haoyu Wang and Yangchen Lu

Mathematics and Computers in Simulation (MATCOM), 2024, vol. 219, issue C, 164-184

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.

Keywords: Feature selection; Swarm intelligent algorithm; Moth flame optimizer; Exploration and exploitation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:219:y:2024:i:c:p:164-184

DOI: 10.1016/j.matcom.2023.12.022

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