A new two-layer nearest neighbor selection method for kNN classifier

doi:10.1016/j.knosys.2021.107604

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	A new two-layer nearest neighbor selection method for kNN classifier
	Wang, Yikun 2; Pan, Zhibin 1,2; Dong, Jing 3
刊名	KNOWLEDGE-BASED SYSTEMS
	2022-01-10
卷号	235 页码:14
关键词	kNN classifier Two-layer nearest neighbor rule First-layer neighborhood Second-layer neighborhood Extended neighborhood
ISSN号	0950-7051
DOI	10.1016/j.knosys.2021.107604
通讯作者	Pan, Zhibin(zbpan@mail.xjtu.edu.cn)
英文摘要	The k-nearest neighbor (kNN) classifier is a classical classification algorithm that has been applied in many fields. However, the performance of the kNN classifier is limited by a simple neighbor selection method, called nearest neighbor (NN) rule, where only the neighborhood of the query is considered when selecting the nearest neighbors of the query. In other words, the NN rule only uses one-layer neighborhood information of the query. In this paper, we propose a new neighbor selection method based on two-layer neighborhood information, called two-layer nearest neighbor (TLNN) rule. The neighborhood of the query and the neighborhoods of all selected training instances in this neighborhood are considered simultaneously, then the two-layer nearest neighbors of the query are determined according to the distance, distribution relationship, and backward nearest neighbor relationship between the query and all selected training instances in the above neighborhoods. In order to verify the effectiveness of the proposed TLNN rule, a k-two-layer nearest neighbor (kTLNN) classifier is proposed to measure the classification ability of the two-layer nearest neighbors. Extensive experiments on twenty real-world datasets from UCI and KEEL repositories show that the kTLNN classifier outperforms not only the kNN classifier but also seven other state-of-the-art NN-based classifiers. (C) 2021 Elsevier B.V. All rights reserved.
资助项目	National Natural Sci-ence Foundation of China[U1903213] ; Key Sci-ence and Technology Program of Shaanxi Province, China[2020GY-005] ; Zhejiang Provincial Commonweal Project, China[LGF21F030002] ; Open Project of the National Laboratory of Pattern Recognition, China[202100033]
WOS关键词	ALGORITHMS ; RULE
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000718126500008
资助机构	National Natural Sci-ence Foundation of China ; Key Sci-ence and Technology Program of Shaanxi Province, China ; Zhejiang Provincial Commonweal Project, China ; Open Project of the National Laboratory of Pattern Recognition, China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46527]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Pan, Zhibin
作者单位	1.Xi An Jiao Tong Univ, Res Inst, Quzhou, Zhejiang, Peoples R China 2.Xi An Jiao Tong Univ, Fac Elect & Informat Engn, Xian 710049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yikun,Pan, Zhibin,Dong, Jing. A new two-layer nearest neighbor selection method for kNN classifier[J]. KNOWLEDGE-BASED SYSTEMS,2022,235:14.
APA	Wang, Yikun,Pan, Zhibin,&Dong, Jing.(2022).A new two-layer nearest neighbor selection method for kNN classifier.KNOWLEDGE-BASED SYSTEMS,235,14.
MLA	Wang, Yikun,et al."A new two-layer nearest neighbor selection method for kNN classifier".KNOWLEDGE-BASED SYSTEMS 235(2022):14.