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On Learning similarity relations in fuzzy case-based reasoning

AutorArmengol, Eva ; Esteva, Francesc ; Godo, Lluis ; Torra, Vicenç
Palabras claveFuzzy case-based reasoning
Aggregation Case-based reasoning
Similarity relation
Fecha de publicación2004
EditorSpringer
CitaciónTransactions on Rough Sets II: Rough Sets and Fuzzy Sets. LNCS 3135: 14- 32 (2004)
ResumenCase-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.
URIhttp://hdl.handle.net/10261/160966
Identificadoresdoi: 10.1007/978-3-540-27778-1_2
issn: 0302-9743
isbn: 978-3-540-23990-1
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