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

Authors

  • Safaa Bouamira Computer Science and Systems Laboratory (LIS), Department of Mathematics and Computer Science, Faculty of Sciences Ain Chock, University Hassan II of Casablanca, Morocco
  • Hasna Chamlal Computer Science and Systems Laboratory (LIS), Department of Mathematics and Computer Science, Faculty of Sciences Ain Chock, University Hassan II of Casablanca, Morocco
  • Tayeb Ouaderhman Computer Science and Systems Laboratory (LIS), Department of Mathematics and Computer Science, Faculty of Sciences Ain Chock, University Hassan II of Casablanca, Morocco

DOI:

https://doi.org/10.19139/soic-2310-5070-2548

Keywords:

Feature Selection, Filters, High Dimensional Datasets

Abstract

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 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

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Published

2025-07-25

How to Cite

Bouamira, S., Chamlal, H., & Ouaderhman, T. (2025). Comparison of filter techniques for feature selection in high-dimensional data. Statistics, Optimization & Information Computing, 15(2), 1000–1011. https://doi.org/10.19139/soic-2310-5070-2548

Issue

Vol. 15 No. 2 (2026): I2CEAI24

Section

Research Articles

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