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<p>This study proposes an Enhanced Binary Kepler Optimization Algorithm (BKOA-MUT) improves feature selection (FS) by integrating Kepler’s planetary motion laws with DE/rand and DE/best Mutation Approach. BKOA-MUT balances exploration and exploitation, effectively guiding search for optimal feature subsets. BKOA-MUT was evaluated using <i>k</i>-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) on 25 UCI benchmarks, including three large-scale ones. It outperformed recent Meta-heuristic Algorithms (MHAs) in accuracy, feature reduction, and computational efficiency. The algorithm showed rapid convergence, minimal feature selection, and scalability, making it a robust and adaptable tool for enhancing FS in machine learning, validated through the Wilcoxon rank-sum test.</p>

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Enhanced Binary Kepler Optimization Algorithm for effective feature selection of supervised learning classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月15日

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  • Amr A. Abd El-Mageed,
  • Amr A. Abohany,
  • Khalid M. Hosny

摘要

This study proposes an Enhanced Binary Kepler Optimization Algorithm (BKOA-MUT) improves feature selection (FS) by integrating Kepler’s planetary motion laws with DE/rand and DE/best Mutation Approach. BKOA-MUT balances exploration and exploitation, effectively guiding search for optimal feature subsets. BKOA-MUT was evaluated using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) on 25 UCI benchmarks, including three large-scale ones. It outperformed recent Meta-heuristic Algorithms (MHAs) in accuracy, feature reduction, and computational efficiency. The algorithm showed rapid convergence, minimal feature selection, and scalability, making it a robust and adaptable tool for enhancing FS in machine learning, validated through the Wilcoxon rank-sum test.