Learning Fuzzy Classification Rules from Data

  • Hans Roubos
  • Magne Setnes
  • Janos Abonyi
Part of the Advances in Soft Computing book series (AINSC, volume 9)


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. 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, and a GA is used for model tuning. An application to the Wine data classification problem is shown.


Genetic Algorithm Membership Function Feature Selection Rule Base Feature Ranking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Hans Roubos
    • 1
  • Magne Setnes
    • 2
  • Janos Abonyi
    • 3
  1. 1.Control LaboratoryDelft University of Technology, ITSDelftThe Netherlands
  2. 2.Heineken Technical ServicesR&DZoeterwoudeThe Netherlands
  3. 3.Department of Process EngineeringUniversity of VeszpremVeszpremHungary

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