Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory

doi:10.1109/ACCESS.2019.2953298

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory
	Wei, Bo 2,3; Zhang, Wensheng 3; Xia, Xuewen 1; Zhang, Yinglong 1; Yu, Fei 1; Zhu, Zhiliang 2
刊名	IEEE ACCESS
	2019
卷号	7 页码:166066-166078
关键词	Combinatorial optimization feature selection global optimization learning memory particle swarm optimization
ISSN号	2169-3536
DOI	10.1109/ACCESS.2019.2953298
通讯作者	Xia, Xuewen(xwxia@whu.edu.cn)
英文摘要	Feature selection is an important pre-processing step in machine learning and data mining tasks, which improves the performance of the learning models by removing redundant and irrelevant features. Many feature selection algorithms have been widely studied, including greedy and random search approaches, to find a subset of the most important features for fulfilling a particular task (i.e., classification and regression). As a powerful swarm-based meta-heuristic method, particle swarm optimization (PSO) is reported to be suitable for optimization problems with continuous search space. However, the traditional PSO has rarely been applied to feature selection as a discrete space search problem. In this paper, a novel feature selection algorithm based on PSO with learning memory (PSO-LM) is proposed. The goal of the learning memory strategy is designed to inherit much more useful knowledge from those individuals who have higher fitness and offer faster progress, and the genetic operation is used to balance the local exploitation and the global exploration of the algorithm. Moreover, the k-nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle. The proposed method has been evaluated on some international standard data sets, and the results demonstrated its superiority compared with those wrapper-based feature selection methods.
资助项目	National Natural Science Foundation of China[61806204] ; National Natural Science Foundation of China[61762036] ; National Natural Science Foundation of China[61663009]
WOS关键词	GENETIC ALGORITHM ; CLASSIFICATION ; PERFORMANCE ; RELEVANCE ; SEARCH
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000509490900001
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/29549]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Xia, Xuewen
作者单位	1.Minnan Normal Univ, Coll Phys & Informat Engn, Zhangzhou 363000, Peoples R China 2.East China Jiaotong Univ, Sch Software, Nanchang 330013, Jiangxi, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wei, Bo,Zhang, Wensheng,Xia, Xuewen,et al. Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory[J]. IEEE ACCESS,2019,7:166066-166078.
APA	Wei, Bo,Zhang, Wensheng,Xia, Xuewen,Zhang, Yinglong,Yu, Fei,&Zhu, Zhiliang.(2019).Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory.IEEE ACCESS,7,166066-166078.
MLA	Wei, Bo,et al."Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory".IEEE ACCESS 7(2019):166066-166078.