Mixed integer linear programming for feature selection in support vector machine

This work focuses on support vector machine (SVM) with feature selection. A MILP formula- tion is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modeled in this formulation by including a budget constraint that sets in advance a limit on th...

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Detalhes bibliográficos
Autores: Labbé, Martine, Martínez Merino, Luisa Isabel, Rodríguez Chía, Antonio Manuel
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/138253
Acesso em linha:https://hdl.handle.net/11441/138253
https://doi.org/10.1016/j.dam.2018.10.025
Access Level:acceso abierto
Palavra-chave:Mathematical programming
Kernel search algorithm
Supervised classification
Support vector machine
Feature selection
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spelling Mixed integer linear programming for feature selection in support vector machineLabbé, MartineMartínez Merino, Luisa IsabelRodríguez Chía, Antonio ManuelMathematical programmingKernel search algorithmSupervised classificationSupport vector machineFeature selectionThis work focuses on support vector machine (SVM) with feature selection. A MILP formula- tion is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modeled in this formulation by including a budget constraint that sets in advance a limit on the number of features to be used in the classification process. We propose both an exact and a heuristic procedure to solve this formulation in an efficient way. Finally, the validation of the model is done by checking it with some well-known data sets and comparing it with classical classification methods.ElsevierEstadística e Investigación Operativa2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/138253https://doi.org/10.1016/j.dam.2018.10.025reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésDiscrete Applied Mathematics, 261, 276-304.https://doi.org/10.1016/j.dam.2018.10.025info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1382532026-06-17T12:51:07Z
dc.title.none.fl_str_mv Mixed integer linear programming for feature selection in support vector machine
title Mixed integer linear programming for feature selection in support vector machine
spellingShingle Mixed integer linear programming for feature selection in support vector machine
Labbé, Martine
Mathematical programming
Kernel search algorithm
Supervised classification
Support vector machine
Feature selection
title_short Mixed integer linear programming for feature selection in support vector machine
title_full Mixed integer linear programming for feature selection in support vector machine
title_fullStr Mixed integer linear programming for feature selection in support vector machine
title_full_unstemmed Mixed integer linear programming for feature selection in support vector machine
title_sort Mixed integer linear programming for feature selection in support vector machine
dc.creator.none.fl_str_mv Labbé, Martine
Martínez Merino, Luisa Isabel
Rodríguez Chía, Antonio Manuel
author Labbé, Martine
author_facet Labbé, Martine
Martínez Merino, Luisa Isabel
Rodríguez Chía, Antonio Manuel
author_role author
author2 Martínez Merino, Luisa Isabel
Rodríguez Chía, Antonio Manuel
author2_role author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
dc.subject.none.fl_str_mv Mathematical programming
Kernel search algorithm
Supervised classification
Support vector machine
Feature selection
topic Mathematical programming
Kernel search algorithm
Supervised classification
Support vector machine
Feature selection
description This work focuses on support vector machine (SVM) with feature selection. A MILP formula- tion is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modeled in this formulation by including a budget constraint that sets in advance a limit on the number of features to be used in the classification process. We propose both an exact and a heuristic procedure to solve this formulation in an efficient way. Finally, the validation of the model is done by checking it with some well-known data sets and comparing it with classical classification methods.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/138253
https://doi.org/10.1016/j.dam.2018.10.025
url https://hdl.handle.net/11441/138253
https://doi.org/10.1016/j.dam.2018.10.025
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Discrete Applied Mathematics, 261, 276-304.
https://doi.org/10.1016/j.dam.2018.10.025
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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