A Hybrid Algorithm Based on PSO and GA for Feature Selection

• Published in: Journal of cyber security (Henderson, Nev.) Vol. 3; no. 2; pp. 117 - 124
• Main Authors: Xue, Yu, Aouari, Asma, F. Mansour, Romany, Su, Shoubao
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2021
• Subjects: Algorithms; Classification; Computing time; Data mining; Genetic algorithms; Heuristic methods; Machine learning; Particle swarm optimization
• ISSN: 2579-0064; 2579-0072; 2579-0064
• DOI: 10.32604/jcs.2021.017018

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.