Detecting relevant variables and interactions in supervised classification

The widely used Support Vector Machine (SVM) method has shown to yield good results in Supervised Classification problems. When the interpretability is an important issue, then classification methods such as Classification Trees (CART) might be more attractive, since they are designed to detect the...

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Detalles Bibliográficos
Autores: Carrizosa Priego, Emilio José, Martín Barragán, Belén, Romero Morales, María Dolores
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2011
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/44822
Acceso en línea:http://hdl.handle.net/11441/44822
https://doi.org/10.1016/j.ejor.2010.03.020
Access Level:acceso abierto
Palabra clave:Supervised classification
Interactions
Support vector machines
Binarization
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spelling Detecting relevant variables and interactions in supervised classificationCarrizosa Priego, Emilio JoséMartín Barragán, BelénRomero Morales, María DoloresSupervised classificationInteractionsSupport vector machinesBinarizationThe widely used Support Vector Machine (SVM) method has shown to yield good results in Supervised Classification problems. When the interpretability is an important issue, then classification methods such as Classification Trees (CART) might be more attractive, since they are designed to detect the important predictor variables and, for each predictor variable, the critical values which are most relevant for classification. However, when interactions between variables strongly affect the class membership, CART may yield misleading information. Extending previous work of the authors, in this paper an SVM-based method is introduced. The numerical experiments reported show that our method is competitive against SVM and CART in terms of misclassification rates, and, at the same time, is able to detect critical values and variables interactions which are relevant for classification.Ministerio de Educación y CienciaJunta de AndalucíaElsevierEstadística e Investigación OperativaFQM329: OptimizacionMinisterio de Educación y Ciencia (MEC). EspañaJunta de Andalucía2011info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/44822https://doi.org/10.1016/j.ejor.2010.03.020reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésEuropean Journal of Operational Research, 213 (1), 260-269.MTM2009-14039ECO2008-05080FQM-329http://ac.els-cdn.com/S0377221710002195/1-s2.0-S0377221710002195-main.pdf?_tid=2b8b9492-75ab-11e6-94a4-00000aacb35f&acdnat=1473329051_ae1431607b554431aa5cc92a9a97c7e2info:eu-repo/semantics/openAccessoai:idus.us.es:11441/448222026-06-17T12:51:07Z
dc.title.none.fl_str_mv Detecting relevant variables and interactions in supervised classification
title Detecting relevant variables and interactions in supervised classification
spellingShingle Detecting relevant variables and interactions in supervised classification
Carrizosa Priego, Emilio José
Supervised classification
Interactions
Support vector machines
Binarization
title_short Detecting relevant variables and interactions in supervised classification
title_full Detecting relevant variables and interactions in supervised classification
title_fullStr Detecting relevant variables and interactions in supervised classification
title_full_unstemmed Detecting relevant variables and interactions in supervised classification
title_sort Detecting relevant variables and interactions in supervised classification
dc.creator.none.fl_str_mv Carrizosa Priego, Emilio José
Martín Barragán, Belén
Romero Morales, María Dolores
author Carrizosa Priego, Emilio José
author_facet Carrizosa Priego, Emilio José
Martín Barragán, Belén
Romero Morales, María Dolores
author_role author
author2 Martín Barragán, Belén
Romero Morales, María Dolores
author2_role author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
FQM329: Optimizacion
Ministerio de Educación y Ciencia (MEC). España
Junta de Andalucía
dc.subject.none.fl_str_mv Supervised classification
Interactions
Support vector machines
Binarization
topic Supervised classification
Interactions
Support vector machines
Binarization
description The widely used Support Vector Machine (SVM) method has shown to yield good results in Supervised Classification problems. When the interpretability is an important issue, then classification methods such as Classification Trees (CART) might be more attractive, since they are designed to detect the important predictor variables and, for each predictor variable, the critical values which are most relevant for classification. However, when interactions between variables strongly affect the class membership, CART may yield misleading information. Extending previous work of the authors, in this paper an SVM-based method is introduced. The numerical experiments reported show that our method is competitive against SVM and CART in terms of misclassification rates, and, at the same time, is able to detect critical values and variables interactions which are relevant for classification.
publishDate 2011
dc.date.none.fl_str_mv 2011
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/44822
https://doi.org/10.1016/j.ejor.2010.03.020
url http://hdl.handle.net/11441/44822
https://doi.org/10.1016/j.ejor.2010.03.020
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv European Journal of Operational Research, 213 (1), 260-269.
MTM2009-14039
ECO2008-05080
FQM-329
http://ac.els-cdn.com/S0377221710002195/1-s2.0-S0377221710002195-main.pdf?_tid=2b8b9492-75ab-11e6-94a4-00000aacb35f&acdnat=1473329051_ae1431607b554431aa5cc92a9a97c7e2
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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