Comparison of filter techniques for feature selection in high-dimensional data - Bouamira, Safaa; Chamlal, Hasna; Ouaderhman, Tayeb (Statistics, Optimization & Information Computing, 2025)
Feature selection constitutes a fundamental challenge within machine learning, which has garnered heightened attention owing to the proliferation of high-dimensional datasets. Filtering-based feature selection methods hold crucial importance as they can be seamlessly integrated with any machine learning model and significantly accelerate the runtime of such algorithms. This study investigates the performance of eight distinct filter methods, examining their efficacy across seven high-dimensional datasets, the classification accuracy was assessed through the employment of support vector machines and k-nearest neighbor classifiers, and the Wilcoxon test statistic was applied to confirm the observed results regarding classification accuracy