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...

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Detalles Bibliográficos
Autores: Armengol, Eva, Esteva, Francesc, Godo, Lluis, Torra, Vicenç
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
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spelling On Learning similarity relations in fuzzy case-based reasoningArmengol, EvaEsteva, FrancescGodo, LluisTorra, VicençFuzzy case-based reasoningAggregation case-based reasoningSimilarity relationCase-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.The authors are partially supported by the EU project IBROW (IST-1999-2005) and the CICYT projects STREAMOBILE (TIC2001-0633-C03-02) and e-INSTITUTOR (TIC2000-1414).Peer ReviewedSpringer NatureComisión Interministerial de Ciencia y Tecnología, CICYT (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2018201820042018info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/160966reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1609662026-05-22T06:33:51Z
dc.title.none.fl_str_mv On Learning similarity relations in fuzzy case-based reasoning
title On Learning similarity relations in fuzzy case-based reasoning
spellingShingle On Learning similarity relations in fuzzy case-based reasoning
Armengol, Eva
Fuzzy case-based reasoning
Aggregation case-based reasoning
Similarity relation
title_short On Learning similarity relations in fuzzy case-based reasoning
title_full On Learning similarity relations in fuzzy case-based reasoning
title_fullStr On Learning similarity relations in fuzzy case-based reasoning
title_full_unstemmed On Learning similarity relations in fuzzy case-based reasoning
title_sort On Learning similarity relations in fuzzy case-based reasoning
dc.creator.none.fl_str_mv Armengol, Eva
Esteva, Francesc
Godo, Lluis
Torra, Vicenç
author Armengol, Eva
author_facet Armengol, Eva
Esteva, Francesc
Godo, Lluis
Torra, Vicenç
author_role author
author2 Esteva, Francesc
Godo, Lluis
Torra, Vicenç
author2_role author
author
author
dc.contributor.none.fl_str_mv Comisión Interministerial de Ciencia y Tecnología, CICYT (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Fuzzy case-based reasoning
Aggregation case-based reasoning
Similarity relation
topic Fuzzy case-based reasoning
Aggregation case-based reasoning
Similarity relation
description 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.
publishDate 2004
dc.date.none.fl_str_mv 2004
2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/160966
url http://hdl.handle.net/10261/160966
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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