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Enhanced Binary Kepler Optimization Algorithm for effective feature selection of supervised learning classification
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DOI：10.1186/s40537-025-01125-6.png)
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Abstract
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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.
Keywords:
Feature selection
Meta-heuristic Algorithms
Kepler Optimization Algorithm
DE/rand and DE/best Mutation Approach
Machine learning classification
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Cited Papers
A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering
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