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

文献类型：期刊论文

作者Wei, Bo1,3; Zhang, Wensheng3; Xia, Xuewen2; Zhang, Yinglong2; Yu, Fei2; Zhu, Zhiliang1
刊名IEEE ACCESS
出版日期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
DOI10.1109/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.
WOS关键词GENETIC ALGORITHM ; CLASSIFICATION ; PERFORMANCE ; RELEVANCE ; SEARCH
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研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000509490900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
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.East China Jiaotong Univ, Sch Software, Nanchang 330013, Jiangxi, Peoples R China
2.Minnan Normal Univ, Coll Phys & Informat Engn, Zhangzhou 363000, 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.

入库方式： OAI收割

来源：自动化研究所

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