On Learning similarity relations in fuzzy case-based reasoning

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. © Springer-Verlag 2004.