Comparison of filter techniques for feature selection in high-dimensional data

Feature selection constitutes a fundamental challenge within machine learning, which has garnered heightenedattention owing to the proliferation of high-dimensional datasets. Filtering-based feature selection methods hold crucialimportance as they can be seamlessly integrated with any machine learning model and significantly accelerate the runtimeof such algorithms. This study investigates the performance of eight distinct filter methods, examining their efficacyacross seven high-dimensional datasets, the classification accuracy was assessed through the employment of support vectormachines and k-nearest neighbor classifiers, and the Wilcoxon test statistic was applied to confirm the observed resultsregarding classification accuracy