Strongly agree or strongly disagree? Rating features in support vector machines

In linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are assigned to classes based on the linear combination of the scores and the values of the features. Inspired by discrete psychometric scales, which measure the extent to which a fac...

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Autores: Carrizosa Priego, Emilio José, Nogales Gómez, Amaya, Romero Morales, María Dolores
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2016
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/42775
Acceso en línea:http://hdl.handle.net/11441/42775
https://doi.org/10.1016/j.ins.2015.09.031
Access Level:acceso abierto
Palabra clave:Support vector machines
Mixed integer linear programming
Likert scale
Interpretability
Feature rating level
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spelling Strongly agree or strongly disagree? Rating features in support vector machinesCarrizosa Priego, Emilio JoséNogales Gómez, AmayaRomero Morales, María DoloresSupport vector machinesMixed integer linear programmingLikert scaleInterpretabilityFeature rating levelIn linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are assigned to classes based on the linear combination of the scores and the values of the features. Inspired by discrete psychometric scales, which measure the extent to which a factor is in agreement with a statement, we propose the Discrete Level Support Vector Machine (DILSVM) where the feature scores can only take on a discrete number of values, de fined by the so-called feature rating levels. The DILSVM classifier benefits from interpretability as it can be seen as a collection of Likert scales, one for each feature, where we rate the level of agreement with the positive class. To build the DILSVM classifier, we propose a Mixed Integer Linear Programming approach, as well as a collection of strategies to reduce the building times. Our computational experience shows that the 3-point and the 5-point DILSVM classifiers have comparable accuracy to the SVM with a substantial gain in interpretability and sparsity, thanks to the appropriate choice of the feature rating levels.Ministerio de Economía y CompetitividadJunta de AndalucíaFondo Europeo de Desarrollo RegionalElsevierEstadística e Investigación OperativaFQM329: Optimizacion2016info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/42775https://doi.org/10.1016/j.ins.2015.09.031reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésInformation Sciences, 329 (C), 256-273.info:eu-repo/grantAgreement/MINECO/MTM2012-36163/FQM-329https://www.sciencedirect.com/science/article/pii/S0020025515006854info:eu-repo/semantics/openAccessoai:idus.us.es:11441/427752026-06-17T12:51:07Z
dc.title.none.fl_str_mv Strongly agree or strongly disagree? Rating features in support vector machines
title Strongly agree or strongly disagree? Rating features in support vector machines
spellingShingle Strongly agree or strongly disagree? Rating features in support vector machines
Carrizosa Priego, Emilio José
Support vector machines
Mixed integer linear programming
Likert scale
Interpretability
Feature rating level
title_short Strongly agree or strongly disagree? Rating features in support vector machines
title_full Strongly agree or strongly disagree? Rating features in support vector machines
title_fullStr Strongly agree or strongly disagree? Rating features in support vector machines
title_full_unstemmed Strongly agree or strongly disagree? Rating features in support vector machines
title_sort Strongly agree or strongly disagree? Rating features in support vector machines
dc.creator.none.fl_str_mv Carrizosa Priego, Emilio José
Nogales Gómez, Amaya
Romero Morales, María Dolores
author Carrizosa Priego, Emilio José
author_facet Carrizosa Priego, Emilio José
Nogales Gómez, Amaya
Romero Morales, María Dolores
author_role author
author2 Nogales Gómez, Amaya
Romero Morales, María Dolores
author2_role author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
FQM329: Optimizacion
dc.subject.none.fl_str_mv Support vector machines
Mixed integer linear programming
Likert scale
Interpretability
Feature rating level
topic Support vector machines
Mixed integer linear programming
Likert scale
Interpretability
Feature rating level
description In linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are assigned to classes based on the linear combination of the scores and the values of the features. Inspired by discrete psychometric scales, which measure the extent to which a factor is in agreement with a statement, we propose the Discrete Level Support Vector Machine (DILSVM) where the feature scores can only take on a discrete number of values, de fined by the so-called feature rating levels. The DILSVM classifier benefits from interpretability as it can be seen as a collection of Likert scales, one for each feature, where we rate the level of agreement with the positive class. To build the DILSVM classifier, we propose a Mixed Integer Linear Programming approach, as well as a collection of strategies to reduce the building times. Our computational experience shows that the 3-point and the 5-point DILSVM classifiers have comparable accuracy to the SVM with a substantial gain in interpretability and sparsity, thanks to the appropriate choice of the feature rating levels.
publishDate 2016
dc.date.none.fl_str_mv 2016
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/11441/42775
https://doi.org/10.1016/j.ins.2015.09.031
url http://hdl.handle.net/11441/42775
https://doi.org/10.1016/j.ins.2015.09.031
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Information Sciences, 329 (C), 256-273.
info:eu-repo/grantAgreement/MINECO/MTM2012-36163/
FQM-329
https://www.sciencedirect.com/science/article/pii/S0020025515006854
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 15.300724

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