Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15618
Title: Kernel difference-weighted k-nearest neighbors classification
Authors: Zuo, W
Wang, K
Zhang, H
Zhang, D 
Keywords: K-nearest neighbor
Kernel methods
Pattern classification
Issue Date: 2007
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2007, v. 4682 LNAI, p. 861-870 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Nearest Neighbor (NN) rule is one of the simplest and most important methods in pattern recognition. In this paper, we propose a kernel difference-weighted k-nearest neighbor method (KDF-WKNN) for pattern classification. The proposed method defines the weighted KNN rule as a constrained optimization problem, and then we propose an efficient solution to compute the weights of different nearest neighbors. Unlike distance-weighted KNN which assigns different weights to the nearest neighbors according to the distance to the unclassified sample, KDF-WKNN weights the nearest neighbors by using both the norm and correlation of the differences between the unclassified sample and its nearest neighbors. Our experimental results indicate that KDF-WKNN is better than the original KNN and distanceweighted KNN, and is comparable to some state-of-the-art methods in terms of classification accuracy.
Description: 3rd International Conference on Intelligent Computing, ICIC 2007, Qingdao, 21-24 August 2007
URI: http://hdl.handle.net/10397/15618
ISBN: 9783540742012
ISSN: 0302-9743
EISSN: 1611-3349
Appears in Collections:Conference Paper

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