Binary arithmetic optimization algorithm for feature selection

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 we...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 27; číslo 16; s. 11395 - 11429
Hlavní autoři: Xu, Min, Song, Qixian, Xi, Mingyang, Zhou, Zhaorong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023
Springer Nature B.V
Témata:
Algorithms
Arithmetic
Artificial Intelligence
Combinatorial analysis
Computational Intelligence
Control
Cooperation
Datasets
Engineering
Evolution
Feature selection
Heuristic
Heuristic methods
Literature reviews
Mathematical Logic and Foundations
Mechatronics
Mutation
Optimization
Optimization algorithms
Physics
Robotics
Transfer functions
Feature selection
Transfer function
Binary arithmetic optimization algorithm
Lévy flight
ISSN:1432-7643, 1433-7479
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Shrnutí: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
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08274-x