Open Access
ARTICLE
A Hybrid Algorithm Based on PSO and GA for Feature Selection
Yu Xue1,*, Asma Aouari1, Romany F. Mansour2, Shoubao Su3
1
Nanjing University of Information Science and Technology, Nanjing, 210044, China
2
Department of Mathematics, Faculty of Science, New Valley University, El-Kharja, 72511, Egypt
3
Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing, 211169, China
* Corresponding Author: Yu Xue. Email:
Journal of Cyber Security 2021, 3(2), 117-124. https://doi.org/10.32604/jcs.2021.017018
Received 21 January 2021; Accepted 14 July 2021; Issue published 02 August 2021
Abstract
One of the main problems of machine learning and data
mining is to develop a basic model with a few features, to reduce the
algorithms involved in classification’s computational complexity. In
this paper, the collection of features has an essential importance in the
classification process to be able minimize computational time, which
decreases data size and increases the precision and effectiveness of
specific machine learning activities. Due to its superiority to
conventional optimization methods, several metaheuristics have been
used to resolve FS issues. This is why hybrid metaheuristics help
increase the search and convergence rate of the critical algorithms. A
modern hybrid selection algorithm combining the two algorithms; the
genetic algorithm (GA) and the Particle Swarm Optimization (PSO) to
enhance search capabilities is developed in this paper. The efficacy of
our proposed method is illustrated in a series of simulation phases,
using the UCI learning array as a benchmark dataset.
Keywords
Cite This Article
Y. Xue, A. Aouari, R. F. Mansour and S. Su, "A hybrid algorithm based on pso and ga for feature selection,"
Journal of Cyber Security, vol. 3, no.2, pp. 117–124, 2021. https://doi.org/10.32604/jcs.2021.017018