GA-based construction of fuzzy classifiers using information granules

Do Wan Kim, Ho Jae Lee, Jin Bae Park, Young Hoon Joo

Research output: Contribution to journalArticle

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 is 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). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.

Original languageEnglish
Pages (from-to)187-196
Number of pages10
JournalInternational Journal of Control, Automation and Systems
Volume4
Issue number2
Publication statusPublished - 2006 Jan 1

Fingerprint

Information granules
Classifiers
Fuzzy sets
Genetic algorithms
Fuzzy rules
Tuning

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications

Cite this

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GA-based construction of fuzzy classifiers using information granules. / Kim, Do Wan; Lee, Ho Jae; Park, Jin Bae; Joo, Young Hoon.

In: International Journal of Control, Automation and Systems, Vol. 4, No. 2, 01.01.2006, p. 187-196.

Research output: Contribution to journalArticle

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