A fuzzy measure of similarity for instance-based learning

  • Francisco Botana
Communications 6A Learning and Knowledge Discovery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1609)


Instance-based learning techniques are based on computing distances or similarities between instances with known classification and objects to classify. The nearest instance or instances are used to predict the class of unseen objects. In this paper we present a fuzzy measure of similarity between fuzzy sets and between elements. This measure allows us to obtain a normalized value of the proximity of objects defined by fuzzy features.

In order to test the efficiency of the proposed measure, we use it in a simple instance-based learning system and make a comparison with other measures proposed in literature.


Similarity Measure Fuzzy Measure Implication Operator Fuzzy Relational Equation Fuzzy Feature 
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 1999

Authors and Affiliations

  • Francisco Botana
    • 1
  1. 1.Departamento de Matemática AplicadaUniversidad de VigoPontevedraSpain

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