Volume 7 • Issue 1 • PP: 39–44 • 2024
Advances and Challenges in Feature Selection Methods: A Comprehensive Review
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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This review examines the role of feature selection in data analytics and its growing use in data-intensive fields, including cybersecurity, healthcare, and finance. It surveys foundational concepts and current applications, with particular attention to reducing dimensionality and improving the interpretation of complex relationships in healthcare data. The review covers optimization-based methods, swarm intelligence, and feature-selection approaches for heart-disease diagnosis, intrusion detection, and other classification tasks. It also discusses hybrid Ant Colony Optimization and Grey Wolf Optimizer methods, alongside the Improved Salp Swarm Algorithm (ISSA). The reviewed studies illustrate that feature selection is more than a preprocessing step: it is an important component of machine learning and data science that can improve model efficiency, interpretability, and classification performance. Challenges and open research directions are summarized to support future work.
Keywords
References
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