Feature selection for clustering based fuzzy modeling

A. Chong, T. D. Gedeon, L. T. Koczy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper, we propose a fast feature selection technique for clustering-based fuzzy modeling. The technique involves the creation of 'rough' fuzzy systems quickly from a set of data and chooses the one with the lowest error. The set of features used by the chosen fuzzy system is accepted as the optimal set of features. The effectiveness and efficiency of the proposed technique is validated using artificially generated data.

Original languageEnglish
Title of host publicationRecent Advances in Intelligent Systems and Signal Processing
Pages361-365
Number of pages5
Publication statusPublished - Dec 1 2003

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Keywords

  • Feature analysis
  • Feature selection
  • Fuzzy modeling
  • Input contribution measurement

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Chong, A., Gedeon, T. D., & Koczy, L. T. (2003). Feature selection for clustering based fuzzy modeling. In Recent Advances in Intelligent Systems and Signal Processing (pp. 361-365)