1
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura 35111, Egypt
(CH2100056@dhiet.edu.eg)
2
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura 35111, Egypt
(CH2100197@dhiet.edu.eg)
3
Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
(azaki@jcsis.org)
4
Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
(farisrizk@jcsis.org)
5
Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt
(mmm@ieee.org)
6
Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt
(skenawy@ieee.org)
Abstract :
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.
Keywords :
Feature Selection; Optimization Algorithms; Machine Learning; Artificial Intelligence; Feature Extraction
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| Style | # |
|---|---|
| MLA | Mohamed Ziad Ali, Abdulrahman Abdullah, Ahmed Mohamed Zaki, Faris H. Rizk, Marwa M. Eid, Elsayed M. El-Kenway. "Advances and Challenges in Feature Selection Methods: A Comprehensive Review." Journal of Artificial Intelligence and Metaheuristics, Vol. 7, No. 1, 2024 ,PP. 67-77 (Doi : https://doi.org/10.54216/JAIM.070105) |
| APA | Mohamed Ziad Ali, Abdulrahman Abdullah, Ahmed Mohamed Zaki, Faris H. Rizk, Marwa M. Eid, Elsayed M. El-Kenway. (2024). Advances and Challenges in Feature Selection Methods: A Comprehensive Review. Journal of Journal of Artificial Intelligence and Metaheuristics, 7 ( 1 ), 67-77 (Doi : https://doi.org/10.54216/JAIM.070105) |
| Chicago | Mohamed Ziad Ali, Abdulrahman Abdullah, Ahmed Mohamed Zaki, Faris H. Rizk, Marwa M. Eid, Elsayed M. El-Kenway. "Advances and Challenges in Feature Selection Methods: A Comprehensive Review." Journal of Journal of Artificial Intelligence and Metaheuristics, 7 no. 1 (2024): 67-77 (Doi : https://doi.org/10.54216/JAIM.070105) |
| Harvard | Mohamed Ziad Ali, Abdulrahman Abdullah, Ahmed Mohamed Zaki, Faris H. Rizk, Marwa M. Eid, Elsayed M. El-Kenway. (2024). Advances and Challenges in Feature Selection Methods: A Comprehensive Review. Journal of Journal of Artificial Intelligence and Metaheuristics, 7 ( 1 ), 67-77 (Doi : https://doi.org/10.54216/JAIM.070105) |
| Vancouver | Mohamed Ziad Ali, Abdulrahman Abdullah, Ahmed Mohamed Zaki, Faris H. Rizk, Marwa M. Eid, Elsayed M. El-Kenway. Advances and Challenges in Feature Selection Methods: A Comprehensive Review. Journal of Journal of Artificial Intelligence and Metaheuristics, (2024); 7 ( 1 ): 67-77 (Doi : https://doi.org/10.54216/JAIM.070105) |
| IEEE | Mohamed Ziad Ali, Abdulrahman Abdullah, Ahmed Mohamed Zaki, Faris H. Rizk, Marwa M. Eid, Elsayed M. El-Kenway, Advances and Challenges in Feature Selection Methods: A Comprehensive Review, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 7 , No. 1 , (2024) : 67-77 (Doi : https://doi.org/10.54216/JAIM.070105) |