A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem

Ruba Abu Khurma, Ibrahim Aljarah, Ahmad Sharieh, Mohamed Abd Elaziz, Robertas Damaševičius and Tomas Krilavičius
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Ruba Abu Khurma: King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
Ibrahim Aljarah: King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
Ahmad Sharieh: King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
Mohamed Abd Elaziz: Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
Robertas Damaševičius: Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
Tomas Krilavičius: Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania

Mathematics, 2022, vol. 10, issue 3, 1-45

Abstract: This survey is an effort to provide a research repository and a useful reference for researchers to guide them when planning to develop new Nature-inspired Algorithms tailored to solve Feature Selection problems (NIAs-FS). We identified and performed a thorough literature review in three main streams of research lines: Feature selection problem, optimization algorithms, particularly, meta-heuristic algorithms, and modifications applied to NIAs to tackle the FS problem. We provide a detailed overview of 156 different articles about NIAs modifications for tackling FS. We support our discussions by analytical views, visualized statistics, applied examples, open-source software systems, and discuss open issues related to FS and NIAs. Finally, the survey summarizes the main foundations of NIAs-FS with approximately 34 different operators investigated. The most popular operator is chaotic maps. Hybridization is the most widely used modification technique. There are three types of hybridization: Integrating NIA with another NIA, integrating NIA with a classifier, and integrating NIA with a classifier. The most widely used hybridization is the one that integrates a classifier with the NIA. Microarray and medical applications are the dominated applications where most of the NIA-FS are modified and used. Despite the popularity of the NIAs-FS, there are still many areas that need further investigation.

Keywords: feature selection; evolutionary algorithms; nature inspired algorithms; meta-heuristic optimization; computational intelligence; soft computing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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