Learning Similarity Metrics from Case Solution Similarity

  • Carlos Morell
  • Rafael Bello
  • Ricardo Grau
  • Yanet Rodríguez
Conference paper

DOI: 10.1007/11874850_44

Volume 4140 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Morell C., Bello R., Grau R., Rodríguez Y. (2006) Learning Similarity Metrics from Case Solution Similarity. In: Sichman J.S., Coelho H., Rezende S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. Lecture Notes in Computer Science, vol 4140. Springer, Berlin, Heidelberg

Abstract

Defining similarity metrics is one of the most important tasks when developing Case Based Reasoning (CBR) systems. The performance of the system heavily depends on the correct definition of its similarity metric. To reduce this sensitivity, similarity functions are parameterized with weights for features. Most approaches to learning feature weights assume CBR systems for classification tasks. In this paper we propose the use of similarity between case solutions as a heuristic to estimate similarity between case descriptions. This estimation is used to adjust weights for features. We present an experiment in the domain of Case Based Process Planning that shows the effectiveness of this approach.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carlos Morell
    • 1
  • Rafael Bello
    • 1
  • Ricardo Grau
    • 1
  • Yanet Rodríguez
    • 1
  1. 1.Computer Sciences Department.Universidad Central de Las VillasCuba