Learning similarity measure of nominal features in CBR classifiers

Yan Li, Chi Keung Simon Shiu, Sankar Kumar Pal, James Nga Kwok Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings
Pages780-785
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2005
Event1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005 - Kolkata, India
Duration: 20 Dec 200522 Dec 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3776 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005
CountryIndia
CityKolkata
Period20/12/0522/12/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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