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The monarch butterfly optimization algorithm for solving feature selection problems

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Abstract

Feature selection (FS) is considered to be a hard optimization problem in data mining and some artificial intelligence fields. It is a process where rather than studying all of the features of a whole dataset, some associated features of a problem are selected, the aim of which is to increase classification accuracy and reduce computational time. In this paper, a recent optimization algorithm, the monarch butterfly optimization (MBO) algorithm, is implemented with a wrapper FS method that uses the k-nearest neighbor (KNN) classifier. Experiments were implemented on 18 benchmark datasets. The results showed that, in comparison with four metaheuristic algorithms (WOASAT, ALO, GA and PSO), MBO was superior, giving a high rate of classification accuracy of, on average, 93% for all datasets as well as reducing the selection size significantly. Therefore, the use of the MBO to solve the FS problems has been proven through the results obtained to be effective and highly efficient in this field, and the results have also proven the strength of the balance between global and local search of MBO.

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Acknowledgements

The research reported in this publication was supported by the Deanship of Scientific Research at Al-Balqa Applied University in Jordan.

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Authors and Affiliations

  1. Department of Computer Science, Al-Balqa Applied University, Salt, Jordan

    Mohammed Alweshah, Saleh Al Khalaileh & Abdelaziz I. Hammouri

  2. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

    Ammar Almomani & Mohammed Azmi Al-Betar

  3. Department of Computer Engineering, National Institute of Technology, Kurukshetra, India

    Brij B. Gupta

  4. Department of Computer Science and Information Engineering, Asia University, Taizhong, Taiwan

    Brij B. Gupta

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  1. Mohammed Alweshah
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  2. Saleh Al Khalaileh
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Alweshah, M., Khalaileh, S.A., Gupta, B.B. et al. The monarch butterfly optimization algorithm for solving feature selection problems. Neural Comput & Applic 34, 11267–11281 (2022). https://doi.org/10.1007/s00521-020-05210-0

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