A Chaos-Enhanced Binary Newton–Raphson Optimizer for High-Dimensional Sensor Data Feature Selection

Feature selection is crucial for high-dimensional sensor and biomedical data because it reduces redundancy, improves generalization, and supports interpretable biomarker discovery. In this study, we propose a Binary Chaos-Enhanced Newton–Raphson-Based Optimizer (BCNRBO) for wrapper-based feature selection. The method integrates chaotic search dynamics, a Hamming-distance-based Dynamic Potential mechanism, and a new binary transfer function to enhance exploration and prevent premature convergence. BCNRBO was evaluated on 26 benchmark datasets using a variety of classifiers, including K-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM) classifiers. The proposed method consistently achieved competitive or superior classification performance while selecting fewer features than competing binary metaheuristic methods. In particular, BCNRBO consistently achieved the best feature reduction performance across all classifiers and secured the top Friedman rankings for DT, NB, and SVM, demonstrating its overall effectiveness. Statistical tests confirmed significant improvements over competing methods in most pairwise comparisons. These results suggest that BCNRBO is a promising feature selection strategy for sensor-derived biomedical and neurorehabilitation data, where compact and reliable digital biomarkers are needed.