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• W4376878640 endingPage "11429" @default.
• W4376878640 startingPage "11395" @default.
• W4376878640 abstract "Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some meta-heuristic algorithms have shown effectiveness in solving hard combinatorial optimization problems. As the arithmetic optimization algorithm only performs well in dealing with continuous optimization problems, multiple binary arithmetic optimization algorithms (BAOAs) utilizing different strategies are proposed to perform feature selection. First, six algorithms are formed based on six different transfer functions by converting the continuous search space to the discrete search space. Second, in order to enhance the speed of searching and the ability of escaping from the local optima, six other algorithms are further developed by integrating the transfer functions and Lévy flight. Based on 20 common University of California Irvine (UCI) datasets, the performance of our proposed algorithms in feature selection is evaluated, and the results demonstrate that BAOA_S1LF is the most superior among all the proposed algorithms. Moreover, the performance of BAOA_S1LF is compared with other meta-heuristic algorithms on 26 UCI datasets, and the corresponding results show the superiority of BAOA_S1LF in feature selection. Source codes of BAOA_S1LF are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/124545-binary-arithmetic-optimization-algorithm" @default.
• W4376878640 created "2023-05-18" @default.
• W4376878640 creator A5002421657 @default.
• W4376878640 creator A5058663918 @default.
• W4376878640 creator A5060187094 @default.
• W4376878640 creator A5061241637 @default.
• W4376878640 date "2023-05-17" @default.
• W4376878640 modified "2023-09-30" @default.
• W4376878640 title "Binary arithmetic optimization algorithm for feature selection" @default.
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• W4376878640 doi "https://doi.org/10.1007/s00500-023-08274-x" @default.
• W4376878640 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37362265" @default.
• W4376878640 hasPublicationYear "2023" @default.
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