Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10830
Title: Dissimilarity learning for nominal data
Authors: Cheng, V
Li, CH
Kwok, JT
Li, CK
Keywords: Classifiers
Dissimilarities
Distance measure
Nominal attributes
Pattern classification
Issue Date: 2004
Publisher: Elsevier
Source: Pattern recognition, 2004, v. 37, no. 7, p. 1471-1477 How to cite?
Journal: Pattern recognition 
Abstract: Defining a good distance (dissimilarity) measure between patterns is of crucial importance in many classification and clustering algorithms. While a lot of work has been performed on continuous attributes, nominal attributes are more difficult to handle. A popular approach is to use the value difference metric (VDM) to define a real-valued distance measure on nominal values. However, VDM treats the attributes separately and ignores any possible interactions among attributes. In this paper, we propose the use of adaptive dissimilarity matrices for measuring the dissimilarities between nominal values. These matrices are learned via optimizing an error function on the training samples. Experimental results show that this approach leads to better classification performance. Moreover, it also allows easier interpretation of (dis)similarity between different nominal values.
URI: http://hdl.handle.net/10397/10830
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2003.12.015
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

28
Last Week
0
Last month
0
Citations as of Aug 15, 2017

WEB OF SCIENCETM
Citations

11
Last Week
0
Last month
Citations as of Aug 20, 2017

Page view(s)

29
Last Week
0
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



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