Similarity measures over refinement graphs

Similarity also plays a crucial role in support vector machines. Similarity assessment plays a key role in lazy learning methods such as k-nearest neighbor or case-based reasoning. In this paper we will show how refinement graphs, that were originally introduced for inductive learning, can be employ...

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Detalhes bibliográficos
Autores: Ontañón, Santiago, Plaza, Enric
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2012
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/138171
Acesso em linha:http://hdl.handle.net/10261/138171
Access Level:acceso abierto
Palavra-chave:Lazy learning
Refinement graphs
Similarity measures
Feature terms
Case-based reasoning
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spelling Similarity measures over refinement graphsOntañón, SantiagoPlaza, EnricLazy learningRefinement graphsSimilarity measuresFeature termsCase-based reasoningSimilarity also plays a crucial role in support vector machines. Similarity assessment plays a key role in lazy learning methods such as k-nearest neighbor or case-based reasoning. In this paper we will show how refinement graphs, that were originally introduced for inductive learning, can be employed to assess and reason about similarity. We will define and analyze two similarity measures, S λ and S π, based on refinement graphs. The anti-unification-based similarity, S λ, assesses similarity by finding the anti-unification of two instances, which is a description capturing all the information common to these two instances. The property-based similarity, S π, is based on a process of disintegrating the instances into a set of properties, and then analyzing these property sets. Moreover these similarity measures are applicable to any representation language for which a refinement graph that satisfies the requirements we identify can be defined. Specifically, we present a refinement graph for feature terms, in which several languages of increasing expressiveness can be defined. The similarity measures are empirically evaluated on relational data sets belonging to languages of different expressiveness. © 2011 The Author(s).Support for this work came from the project Next-CBR TIN2009-13692-C03-01 (co-sponsored by EU FEDER funds)Peer ReviewedKluwer Academic PublishersMinisterio de Educación y Ciencia (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2016201620122016info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/138171reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1381712026-05-22T06:33:51Z
dc.title.none.fl_str_mv Similarity measures over refinement graphs
title Similarity measures over refinement graphs
spellingShingle Similarity measures over refinement graphs
Ontañón, Santiago
Lazy learning
Refinement graphs
Similarity measures
Feature terms
Case-based reasoning
title_short Similarity measures over refinement graphs
title_full Similarity measures over refinement graphs
title_fullStr Similarity measures over refinement graphs
title_full_unstemmed Similarity measures over refinement graphs
title_sort Similarity measures over refinement graphs
dc.creator.none.fl_str_mv Ontañón, Santiago
Plaza, Enric
author Ontañón, Santiago
author_facet Ontañón, Santiago
Plaza, Enric
author_role author
author2 Plaza, Enric
author2_role author
dc.contributor.none.fl_str_mv Ministerio de Educación y Ciencia (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Lazy learning
Refinement graphs
Similarity measures
Feature terms
Case-based reasoning
topic Lazy learning
Refinement graphs
Similarity measures
Feature terms
Case-based reasoning
description Similarity also plays a crucial role in support vector machines. Similarity assessment plays a key role in lazy learning methods such as k-nearest neighbor or case-based reasoning. In this paper we will show how refinement graphs, that were originally introduced for inductive learning, can be employed to assess and reason about similarity. We will define and analyze two similarity measures, S λ and S π, based on refinement graphs. The anti-unification-based similarity, S λ, assesses similarity by finding the anti-unification of two instances, which is a description capturing all the information common to these two instances. The property-based similarity, S π, is based on a process of disintegrating the instances into a set of properties, and then analyzing these property sets. Moreover these similarity measures are applicable to any representation language for which a refinement graph that satisfies the requirements we identify can be defined. Specifically, we present a refinement graph for feature terms, in which several languages of increasing expressiveness can be defined. The similarity measures are empirically evaluated on relational data sets belonging to languages of different expressiveness. © 2011 The Author(s).
publishDate 2012
dc.date.none.fl_str_mv 2012
2016
2016
2016
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/10261/138171
url http://hdl.handle.net/10261/138171
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 Kluwer Academic Publishers
publisher.none.fl_str_mv Kluwer Academic Publishers
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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score 15.198674

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