Design of fussy rule-based classifier: Pruning and learning

Do Wan Kim, Jin Bae Park, Young Hoon Joo

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


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
Title of host publicationFuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783540283126
Publication statusPublished - 2006
Event2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsa, China
Duration: 2005 Aug 272005 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3613 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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