Design of fuzzy rule-based classifier: Pruning and learning

Do Wan Kim, Jin Bae Park, Young Hoon Joo

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


This paper presents new pruning and learning methods for the fuzzy rule-based classifier. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are adjusted by using the gradient descent method so that the misclassified feature vectors are correctly recategorized. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

Original languageEnglish
Pages (from-to)416-425
Number of pages10
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Issue numberPART I
Publication statusPublished - 2005
EventSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China
Duration: 2005 Aug 272005 Aug 29

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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