Paper Info
Title
Pseudo nearest neighbor rule for pattern classification
Abstract
In this paper, we propose a new pseudo nearest neighbor classification rule (PNNR). It is different from the previous nearest neighbor rule (NNR), this new rule utilizes the distance weighted local learning in each class to get a new nearest neighbor of the unlabeled pattern-pseudo nearest neighbor (PNN), and then assigns the label associated with the PNN for the unlabeled pattern using the NNR. The proposed PNNR is compared with the k-NNR, distance weighted k-NNR, and the local mean-based nonparametric classification [Mitani, Y., & Hamamoto, Y. (2006). A local mean-based nonparametric classifier. Pattern Recognition Letters, 27, 1151-1159] in terms of the classification accuracy on the unknown patterns. Experimental results confirm the validity of this new classification rule even in practical situations.
Year
DOI
Venue
2009
10.1016/j.eswa.2008.02.003
Expert Syst. Appl.
Keywords
Field
DocType
pseudo nearest neighbor classification rule (pnnr),the k -nearest neighbor classification rule ( k -nnr),new classification rule,neighbor classification rule,local learning,pattern classification,classification accuracy,local mean-based nonparametric classification,pseudo nearest neighbor (pnn),local mean-based nonparametric classifier,new nearest neighbor,new pseudo,the local mean-based learning,new rule,previous nearest neighbor rule,distance weighted k -nearest neighbor rule,pattern recognition,nearest neighbor,k nearest neighbor
k-nearest neighbors algorithm,Data mining,Classification rule,Pattern recognition,Ball tree,Best bin first,Computer science,Nearest neighbor graph,Nearest-neighbor chain algorithm,Artificial intelligence,Large margin nearest neighbor,Nearest neighbor search
Journal
Volume
Issue
ISSN
36
2
Expert Systems With Applications
Citations 
PageRank 
References 
26
1.18
12
Authors
3
Name
Order
Citations
PageRank
Yong Zeng1984.75
Yupu Yang233225.20
Liang Zhao310013.74