Fuzzy rules from ant-inspired computation

M. Galea, Qiang Shen

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

Abstract

A new approach to fuzzy rule induction from historical data is presented. The implemented system - FRANTIC - is a tested on a simple classification problem against a fuzzy tree induction algorithm, a genetic algorithm, and a numerical method for inducing fuzzy rules based on fuzzy subsethood values. The results obtained by FRANTIC indicate comparable or better classification accuracy, superior comprehensibility, and potentially more flexibility when applied to larger data sets. The impact of the knowledge representation used when generating fuzzy rules is also highlighted.
Original languageEnglish
Title of host publicationProceedings of 2004 IEEE International Conference on Fuzzy Systems.
Pages1691-1696 vol.3
Number of pages6
Volume3
DOIs
Publication statusPublished - 1 Jul 2004

Keywords

  • fuzzy set theory
  • genetic algorithms
  • knowledge representation
  • tree data structures
  • ant-inspired computation
  • classification accuracy
  • fuzzy rule
  • fuzzy tree induction algorithm
  • genetic algorithm
  • optimisation
  • Classification tree analysis
  • Clustering algorithms
  • Fuzzy systems
  • Genetic algorithms
  • Induction generators
  • Informatics
  • Insects
  • Knowledge representation
  • Partitioning algorithms
  • System testing

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