A Connectionist Fuzzy Case-Based Reasoning Model

  • Yanet Rodriguez
  • Maria M. Garcia
  • Bernard De Baets
  • Carlos Morell
  • Rafael Bello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


This paper presents a new version of an existing hybrid model for the development of knowledge-based systems, where case-based reasoning is used as a problem solver. Numeric predictive attributes are modeled in terms of fuzzy sets to define neurons in an associative Artificial Neural Network (ANN). After the Fuzzy-ANN is trained, its weights and the membership degrees in the training examples are used to automatically generate a local distance function and an attribute weighting scheme. Using this distance function and following the Nearest Neighbor rule, a new hybrid Connectionist Fuzzy Case-Based Reasoning model is defined. Experimental results show that the model proposed allows to develop knowledge-based systems with a higher accuracy than when using the original model. The model takes the advantages of the approaches used, providing a more natural framework to include expert knowledge by using linguistic terms.


Original Model Membership Degree Linguistic Term Numeric Attribute Predictive Attribute 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yanet Rodriguez
    • 1
  • Maria M. Garcia
    • 1
  • Bernard De Baets
    • 2
  • Carlos Morell
    • 1
  • Rafael Bello
    • 1
  1. 1.Universidad Central de Las VillasSanta ClaraCuba
  2. 2.Ghent UniversityGentBelgium

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