A Hybrid Algorithm Based on PSO and GA for Feature Selection  

作　　者：Yu Xue Asma Aouari Romany F.Mansour Shoubao Su 

机构地区：[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

出　　处：《Journal of Cyber Security》2021年第2期117-124,共8页网络安全杂志（英文）

基　　金：This work was partially supported by the National Natural Science Foundation of China(61876089,61876185,61902281,61375121);the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS301);the Engineering Research Center of Digital Forensics,Ministry of Education,the Key Research and Development Program of Jiangsu Province(BE2020633);the Priority Academic Program Development of Jiangsu Higher Education Institutions。

摘　　要：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.

关 键 词：Evolutionary computation genetic algorithm hybrid approach META-HEURISTIC feature selection particle swarm optimization 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]