Bibliographic Details
| Title: |
Enhanced Binary Kepler Optimization Algorithm for effective feature selection of supervised learning classification. |
| Authors: |
El-Mageed, Amr A. Abd, Abohany, Amr A., Hosny, Khalid M. |
| Source: |
Journal of Big Data; 4/15/2025, Vol. 12 Issue 1, p1-67, 67p |
| Subject Terms: |
KEPLER'S laws, OPTIMIZATION algorithms, METAHEURISTIC algorithms, SUPERVISED learning, MACHINE learning |
| Abstract: |
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. [ABSTRACT FROM AUTHOR] |
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