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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)

Abstract

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.

Keywords

Original Model Membership Degree Linguistic Term Numeric Attribute Predictive Attribute 
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 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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