Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 7 , Issue 1 , PP: 67-77, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Mohamed Ziad Ali 1 * , Abdulrahman Abdullah 2 , Ahmed Mohamed Zaki 3 * , Faris H. Rizk 4 , Marwa M. Eid 5 , Elsayed M. El-Kenway 6
Doi: https://doi.org/10.54216/JAIM.070105
The feature selection area in data analytics is explored through a comprehensive literature review, and the increasing areas that have a data dependency problem and are being resolved with feature selection are highlighted. Review topics of this course cover the foundations to present use cases, for example, cybersecurity, healthcare, and finance. Particularly crucially for the healthcare domain, it reduces the dimensionality and elucidates complex causal links. The further investigation overlaps contemporary techniques, including optimization-based methods, swarm intelligence and algorithms for the diagnosis of heart diseases. The conclusion builds on the practical assessment and underlines research gaps, serving as a basis to set a diversified technological review. This also exhibits new techniques that have released their efficiency in classification environments, for example, hybrid Ant Colony Optimization and the Gray Wolf Optimizer. The ISSA algorithm stands out as a swarm intelligence technique that is best among others. The paper concludes by demonstrating that feature selection goes beyond the preprocessing stage, but it instead stands as a vital part of the fields of machine learning and data science and thus aids the researchers in both retrospective analysis and forthcoming projects.
Feature Selection , Optimization Algorithms , Machine Learning , Artificial Intelligence , Feature Extraction
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