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• W4362677007 abstract "With the whirlwind evolution of technology, the quantity of stored data within datasets is rapidly expanding. As a result, extracting crucial and relevant information from said datasets is a gruelling task. Feature selection is a critical preprocessing task for machine learning to reduce the excess data in a set. This research presents a novel quasi-reflection learning arithmetic optimization algorithm - firefly search, an enhanced version of the original arithmetic optimization algorithm. Quasi-reflection learning mechanism was implemented for enhancement of population diversity, while firefly algorithm metaheuristics were used to improve the exploitation abilities of the original arithmetic optimization algorithm. The aim of this wrapper-based method is to tackle a specific classification problem by selecting an optimal feature subset. The proposed algorithm is tested and compared with various well-known methods on ten unconstrained benchmark functions, then on twenty-one standard datasets gathered from the University of California, Irvine Repository and Arizona State University. Additionally, the proposed approach is applied to the Corona disease dataset. The experimental results verify the improvements of the presented method and their statistical significance." @default.
• W4362677007 created "2023-04-07" @default.
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• W4362677007 date "2023-04-01" @default.
• W4362677007 modified "2023-10-16" @default.
• W4362677007 title "Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection" @default.
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• W4362677007 doi "https://doi.org/10.1016/j.heliyon.2023.e15378" @default.
• W4362677007 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37101631" @default.
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