Review on Hybrid Swarm Algorithms for Feature Selection

Abubakr S. Issa; Yossra H. Ali; Tarik A. Rashid

doi:10.24996/ijs.2023.64.10.38

Authors

Abubakr S. Issa Computer Science Department, University of Technology, Baghdad , Iraq https://orcid.org/0000-0001-6445-7219
Yossra H. Ali Computer Science & Engineering Department, University of kurdidtan Hewler, KRG, Iraq https://orcid.org/0000-0002-7216-4149
Tarik A. Rashid Computer Science Department, University of Technology, Baghdad , Iraq

DOI:

https://doi.org/10.24996/ijs.2023.64.10.38

Keywords:

Feature selection, Hybrid Swarm intelligence, literature review, classification

Abstract

Feature selection represents one of the critical processes in machine learning (ML). The fundamental aim of the problem of feature selection is to maintain performance accuracy while reducing the dimension of feature selection. Different approaches were created for classifying the datasets. In a range of optimization problems, swarming techniques produced better outcomes. At the same time, hybrid algorithms have gotten a lot of attention recently when it comes to solving optimization problems. As a result, this study provides a thorough assessment of the literature on feature selection problems using hybrid swarm algorithms that have been developed over time (2018-2021). Lastly, when compared with current feature selection procedures, the majority of hybrid algorithms enhance classification accuracy.