| Issue |
E3S Web Conf.
Volume 351, 2022
10th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|
|
|---|---|---|
| Article Number | 01046 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/e3sconf/202235101046 | |
| Published online | 24 May 2022 | |
Feature Selection: A Review and Comparative Study
1 Department of Informatics, UAE, Tetouan Morocco
2 Department of Informatics, USMBA, Fez Morocco
3 Department of Informatics, UAE, Tetouan Morocco
* Corresponding author: younes011991@gmail.com
Feature selection (FS) is an important research topic in the area of data mining and machine learning. FS aims at dealing with the high dimensionality problem. It is the process of selecting the relevant features and removing the irrelevant, redundant and noisy ones, intending to obtain the best performing subset of original features without any transformation. This paper provides a comprehensive review of FS literature intending to supplement insights and recommendations to help readers. Moreover, an empirical study of six well-known feature selection methods is presented so as to critically analyzing their applicability.
Key words: Dimensionality Reduction / Feature Extraction / Feature Selection / Environment
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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