On Learning similarity relations in fuzzy case-based reasoning
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 funct...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2004 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/160966 |
| Acceso en línea: | http://hdl.handle.net/10261/160966 |
| Access Level: | acceso abierto |
| Palabra clave: | Fuzzy case-based reasoning Aggregation case-based reasoning Similarity relation |
| Sumario: | 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. |
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