Please use this identifier to cite or link to this item:
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
ISBN: 9783540742012
ISSN: 0302-9743
EISSN: 1611-3349
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Sep 18, 2017

Page view(s)

Last Week
Last month
Checked on Sep 18, 2017

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.