Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64340
Title: Learning similarity measure of nominal features in CBR classifiers
Authors: Li, Y
Shiu, SCK 
Pal, SK
Liu, JNK
Issue Date: 2005
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), v. 3776, p. 780-785 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Nominal feature is one type of symbolic features, whose feature values are completely unordered. The most often used existing similarity metrics for symbolic features is the Hamming metric, where similarity computation is coarse-grained and may affect the performance of case retrieval and then the classification accuracy. This paper presents a GA-based approach for learning similarity measure of nominal features for CBR classifiers. Based on the learned similarities, the classification accuracy can be improved, and the importance of each nominal feature can be analyzed to enhance the understanding of the used data sets.
Description: 1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005, Kolkata, India, December 20-22, 2005
URI: http://hdl.handle.net/10397/64340
ISBN: 978-3-540-30506-4 (print)
978-3-540-32420-1 (online)
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/11590316_126
Appears in Collections:Conference Paper

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