Learning similarity measure of nominal features in CBR classifiers

Authors:
Yan Li;Simon Chi-Keung Shiu;Sankar Kumar Pal;James Nga-Kwok Liu
Affiliations:
Department of Computing, Hong Kong Polytechnic University, Kowloon, HongKong;Department of Computing, Hong Kong Polytechnic University, Kowloon, HongKong;Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India;Department of Computing, Hong Kong Polytechnic University, Kowloon, HongKong
Venue:
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Year:
2005

Citing 5
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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.