Evolutionary design of fuzzy classifiers using information granules

Do Wan Kim, Jin Bae Park, Young Hoon Joo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA are utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes).

Original languageEnglish
Title of host publicationAI 2005
Subtitle of host publicationAdvances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings
PublisherSpringer Verlag
Pages380-388
Number of pages9
ISBN (Print)3540304622, 9783540304623
DOIs
Publication statusPublished - 2005
Event18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence - Sydney, Australia
Duration: 2005 Dec 52005 Dec 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3809 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence
CountryAustralia
CitySydney
Period05/12/505/12/9

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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