Optimizing Feature Selection with Metaheuristics: Trends, Techniques, and Future Directions
José Barrera-García, Felipe Cisternas-Caneo, Broderick Crawford, Mariam Gómez Sánchez, Ricardo Soto, Marcelo Becerra, Jose Manuel Gomez-Pulido, Alberto Garcés-Jiménez · Frontiers in artificial intelligence and applications · 2024
This paper presents a concise literature review of metaheuristic algorithms in feature selection, spanning publications from 2019 to 2023. The study acknowledges the role of wrapper methods and metaheuristics, noting their ability to yield enhanced results. It highlights the prevalent use of Particle Swarm Optimization, Grey Wolf Optimizer, and Genetic Algorithm in this context. Additionally, the research explores trends and approaches in binarization within metaheuristics, distinguishing between straightforward binarization and more elaborate methods. A central theme of the study is the investigation of hybridization in metaheuristics, demonstrating the use and integration of multiple algorithms in search of improvement in performance. The paper also discusses strategies for refining metaheuristic performance, such as chaotic maps and local search. It examines the emerging domain of multi-objective metaheuristics, which is particularly relevant for addressing real-world problems with competing objectives. The study highlights the dynamic and innovative potential of metaheuristic-based feature selection methodologies.