Learning fuzzy classification rules from labeled data

  • Authors:

  • Johannes A. Roubos;Magne Setnes;Janos Abonyi

  • Affiliations:

  • Control Laboratory, Faculty of Information Technology and Sciences, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands;Heineken Technical Services, R&D, Burgemeester Smeetsweg 1, 2382 PH Zoeterwoude, The Netherlands;Department of Process Engineering, University of Veszprem, P.O. Box 158, H-8201 Veszprem, Hungary

  • Venue:
  • Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
  • Year:
  • 2003

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Abstract

The automatic design of fuzzy rule-based classification systems based on labeled data is considered. It is recognized that both classification performance and interpretability are of major importance and effort is made to keep the resulting rule bases small and comprehensible. For this purpose, an iterative approach for developing fuzzy classifiers is proposed. The initial model is derived from the data and subsequently, feature selection and rule-base simplification are applied to reduce the model, while a genetic algorithm is used for parameter optimization. An application to the Wine data classification problem is shown.