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

doi:10.1016/j.knosys.2021.107604

CASIA OpenIR > 模式识别实验室

	A new two-layer nearest neighbor selection method for kNN classifier
	Wang, Yikun 1; Pan, Zhibin 1,3; Dong, Jing2
发表期刊	KNOWLEDGE-BASED SYSTEMS
ISSN	0950-7051
	2022-01-10
卷号	235 页码:14
通讯作者	Pan, Zhibin(zbpan@mail.xjtu.edu.cn)
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.
关键词	kNN classifier Two-layer nearest neighbor rule First-layer neighborhood Second-layer neighborhood Extended neighborhood
DOI	10.1016/j.knosys.2021.107604
关键词[WOS]	ALGORITHMS ; RULE
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000718126500008
出版者	ELSEVIER
引用统计	被引频次：26[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46528
专题	模式识别实验室
通讯作者	Pan, Zhibin
作者单位	1.Xi An Jiao Tong Univ, Fac Elect & Informat Engn, Xian 710049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Xi An Jiao Tong Univ, Res Inst, Quzhou, Zhejiang, 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.