Mathematical Methods in Feature Selection: A Review

Firuz Kamalov (), Hana Sulieman, Ayman Alzaatreh, Maher Emarly, Hasna Chamlal and Murodbek Safaraliev
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Firuz Kamalov: Department of Electrical Engineering, Canadian University Dubai, Dubai P.O. Box 117781, United Arab Emirates
Hana Sulieman: Department of Mathematics and Statistics, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
Ayman Alzaatreh: Department of Mathematics and Statistics, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
Maher Emarly: Department of Mathematics and Statistics, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
Hasna Chamlal: Computer Science and Systems Laboratory (LIS), Faculty of Sciences Ain Chock, Hassan II University of Casablanca, Casablanca 20360, Morocco
Murodbek Safaraliev: Ural Power Engineering Institute, Ural Federal University, Yekaterinburg 620002, Russia

Mathematics, 2025, vol. 13, issue 6, 1-29

Abstract: Feature selection is essential in machine learning and data science. Recently, there has been a growing effort to apply various mathematical methods to construct novel feature selection algorithms. In this study, we present a comprehensive state-of-the-art review of such techniques. We propose a new mathematical framework-based taxonomy to group the existing literature and provide an analysis of the research in each category from a mathematical perspective. The key frameworks discussed include variance-based methods, regularization methods, and Bayesian methods. By analyzing the strengths and limitations of each technique, we provide insights into their applicability across various domains. The review concludes with emerging trends and future research directions for mathematical methods in feature selection.

Keywords: feature selection; mathematical methods; machine learning; variance; Bayesian; regularization; classification; regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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