Nearest neighbour group-based classification

Samsudin, Noor A. and Bradley, Andrew P. (2010) Nearest neighbour group-based classification. Pattern Recognition, 43 10: 3458-3467. doi:10.1016/j.patcog.2010.05.010

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ209996.pdf UQ209996.pdf application/pdf 48.02KB 0

Author Samsudin, Noor A.
Bradley, Andrew P.
Title Nearest neighbour group-based classification
Journal name Pattern Recognition   Check publisher's open access policy
ISSN 0031-3203
Publication date 2010-10
Sub-type Article (original research)
DOI 10.1016/j.patcog.2010.05.010
Volume 43
Issue 10
Start page 3458
End page 3467
Total pages 10
Editor Ching Y. Suen
Place of publication Oxford, U.K.; New York, U.S.A.
Publisher Pergamon Press
Collection year 2011
Language eng
Formatted abstract
The purpose of group-based classification (GBC) is to determine the class label for a set of test samples, utilising the prior knowledge that the samples belong to same, but unknown class. This can be seen as a simplification of the well studied, but computationally complex, non-sequential compound classification problem. In this paper, we extend three variants of the nearest neighbour algorithm to develop a number of non-parametric group-based classification techniques. The performances of the proposed techniques are then evaluated on both synthetic and real-world data sets and their performance compared with techniques that label test samples individually. The results show that, while no one algorithm clearly outperforms all others on all data sets, the proposed group-based classification techniques have the potential to outperform the individual-based techniques, especially as the (group) size of the test set increases. In addition, it is shown that algorithms that pool information from the whole test set perform better than two-stage approaches that undertake a vote based on the class labels of individual test samples.
© 2010 Elsevier Ltd. All rights reserved
Keyword Group-based classification
Nearest neighbour
Compound classification
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 11 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 15 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Sun, 01 Aug 2010, 00:02:16 EST