Feature selection using binary monarch butterfly optimization

Swarm intelligence algorithms have superior performance in searching for the optimal feature subset, where Monarch Butterfly Optimization (MBO) can solve the continuous optimization problem. However, there exist some defects for MBO such as the limited searchable positions, falling into local optimu...

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Vydáno v:Applied intelligence (Dordrecht, Netherlands) Ročník 53; číslo 1; s. 706 - 727
Hlavní autoři: Sun, Lin, Si, Shanshan, Zhao, Jing, Xu, Jiucheng, Lin, Yaojin, Lv, Zhiying
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.01.2023
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Computer Science
Datasets
Feature selection
Heuristic methods
Machines
Manufacturing
Mechanical Engineering
Mutation
Operators (mathematics)
Optimization
Processes
Swarm intelligence
Transfer functions
Mutation operator
Monarch butterfly optimization
Feature selection
Metaheuristic
ISSN:0924-669X, 1573-7497
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Shrnutí:Swarm intelligence algorithms have superior performance in searching for the optimal feature subset, where Monarch Butterfly Optimization (MBO) can solve the continuous optimization problem. However, there exist some defects for MBO such as the limited searchable positions, falling into local optimum easily and unsolved binary variables. To address these drawbacks, this paper develops two mechanisms to propose several revisions of binary MBO (BMBO) for metaheuristic feature selection. First, to make MBO suitable to solve the feature selection optimization problems, the S-shaped and V-shaped transfer functions are introduced to convert continuous space into binary, and then force the butterfly to move in the binary search space. Two updated positions of the monarch butterfly population are designed based on these above transfer functions respectively to construct two BMBO models, namely BMBO-S and BMBO-V, as the first mechanism of BMBO. Second, the new step length parameter is proposed to update the position of monarch butterfly individuals. To prevent MBO from falling into the local optimum, the local disturbance and group division strategies are added into MBO to construct new BMBO method. It follows that a mutation rate is employed to enhance the detection stage of BMBO, and the mutation operator-based BMBO (BMBO-M) is designed to avoid the premature convergence of MBO. Third, this fitness function is integrated with the KNN classifier and the weight of the feature subset length to rank the selected feature subset, and a metaheuristic feature selection algorithm with BMBO-M is developed. Experiments applied to nineteen low dimensional UCI datasets and seven high dimensional datasets demonstrate our designed algorithm has great classification efficiency when compared with the other related technologies.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-022-03554-9