Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

Abstract

Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities.

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

“…Additionally, ref. [42] compiles a series of research on feature selection, where a taxonomy of objective functions is presented, and related metrics are classified into four categories: classifiers, metaheuristics, features, and statistical tests. This classification facilitates effective comparison of different feature selection methodologies, focusing on metaheuristics' implementation and hybridization strategies.…”

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

“…Feature selection is a common method for recognizing emotions and reducing dimensionality [ 7 , 8 , 9 ]. Metaheuristic algorithms have universal and diverse heuristic strategies [ 10 , 11 ], and they are powerful tools for handling complex optimization problems such as feature selection [ 12 , 13 , 14 ].…”