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A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm

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  • Guangwei Liu
  • Zhiqing Guo
  • Wei Liu
  • Feng Jiang
  • Ensan Fu

Abstract

This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. In Case 2, GJO-GWO was utilized to tackle ten feature selection problems. Experimental results consistently demonstrate that under identical experimental conditions, whether solving complex benchmark functions or addressing feature selection problems, GJO-GWO exhibits smaller means, lower standard deviations, higher classification accuracy, and reduced execution times. These findings affirm the superior optimization performance, classification accuracy, and stability of the GJO-GWO algorithm.

Suggested Citation

  • Guangwei Liu & Zhiqing Guo & Wei Liu & Feng Jiang & Ensan Fu, 2024. "A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-32, January.
  • Handle: RePEc:plo:pone00:0295579
    DOI: 10.1371/journal.pone.0295579
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    References listed on IDEAS

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    1. El-Sayed M. El-kenawy & Fahad Albalawi & Sayed A. Ward & Sherif S. M. Ghoneim & Marwa M. Eid & Abdelaziz A. Abdelhamid & Nadjem Bailek & Abdelhameed Ibrahim, 2022. "Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
    2. El-Sayed M. El-Kenawy & Nima Khodadadi & Seyedali Mirjalili & Tatiana Makarovskikh & Mostafa Abotaleb & Faten Khalid Karim & Hend K. Alkahtani & Abdelaziz A. Abdelhamid & Marwa M. Eid & Takahiko Horiu, 2022. "Metaheuristic Optimization for Improving Weed Detection in Wheat Images Captured by Drones," Mathematics, MDPI, vol. 10(23), pages 1-30, November.
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