Rough learning vector quantization case generation for 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

1 Citation (Scopus)

Abstract

To build competent and efficient CBR classifiers, we develop a case generation approach which integrates fuzzy sets, rough sets and learning vector quantization (LVQ). If the feature values of the cases are numerical, fuzzy sets are firstly used to discretize the feature spaces. Secondly, a fast rough set-based feature selection method is built to identify the significant features. The representative cases (prototypes) are then generated through LVQ learning process on the case bases after feature selection. These prototypes can be also considered as the extracted knowledge which improves the understanding of the case base. Three real life data sets are used in the experiments to demonstrate the effectiveness of this case generation approach.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages128-137
Number of pages10
Publication statusPublished - 1 Dec 2005
Event10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005 - Regina, Canada
Duration: 31 Aug 20053 Sep 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3642 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005
CountryCanada
CityRegina
Period31/08/053/09/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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