On learning similarity relations in fuzzy case-based reasoning

  • Authors:

  • Eva Armengol;Francesc Esteva;Lluís Godo;Vicenç Torra

  • Affiliations:

  • Institut d’Investigació en Intel·ligència Artificial CSIC, Campus UAB s/n, Bellaterra, Catalonia, Spain;Institut d’Investigació en Intel·ligència Artificial CSIC, Campus UAB s/n, Bellaterra, Catalonia, Spain;Institut d’Investigació en Intel·ligència Artificial CSIC, Campus UAB s/n, Bellaterra, Catalonia, Spain;Institut d’Investigació en Intel·ligència Artificial CSIC, Campus UAB s/n, Bellaterra, Catalonia, Spain

  • Venue:
  • Transactions on Rough Sets II
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Case–based reasoning (CBR) is a problem solving technique that puts at work the general principle that similar problems have similar solutions. In particular, it has been proved effective for classification problems. Fuzzy set–based approaches to CBR rely on the existence of a fuzzy similarity functions on the problem description and problem solution domains. In this paper, we study the problem of learning a global similarity measure in the problem description domain as a weighted average of the attribute–based similarities and, therefore, the learning problem consists in finding the weighting vector that minimizes mis–classification. The approach is validated by comparing results with an application of case–based reasoning in a medical domain that uses a different model.