Abrupt change detection with One-Class Time-Adaptive Support Vector Machines

Autores
Grinblat, Guillermo Luis; Uzal, Lucas César; Granitto, Pablo Miguel
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We recently introduced an algorithm for training a sequence of coupled Support Vector Machines which shows promising results in the field of non-stationary classification problems Grinblat, Uzal, Ceccatto, and Granitto (2011). In this paper we analyze its application to the abrupt change detection problem. With this goal, we first introduce and analyze an extension of it to deal with the One-Class Support Vector Machine (OC-SVM) problem, and then discuss its use as an improved abrupt change detection method. Finally, we apply the proposed procedure to artificial and real-world examples, and demonstrate that it is competitive by comparison against other abrupt change detection methods.
Fil: Grinblat, Guillermo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Uzal, Lucas César. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Materia
ABRUPT CHANGE DETECTION
ONE CLASS CLASSIFICATION
SUPPORT VECTOR MACHINE
Nivel de accesibilidad
acceso embargado
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/3196
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/3196
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Abrupt change detection with One-Class Time-Adaptive Support Vector MachinesGrinblat, Guillermo LuisUzal, Lucas CésarGranitto, Pablo MiguelABRUPT CHANGE DETECTIONONE CLASS CLASSIFICATIONSUPPORT VECTOR MACHINEhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1We recently introduced an algorithm for training a sequence of coupled Support Vector Machines which shows promising results in the field of non-stationary classification problems Grinblat, Uzal, Ceccatto, and Granitto (2011). In this paper we analyze its application to the abrupt change detection problem. With this goal, we first introduce and analyze an extension of it to deal with the One-Class Support Vector Machine (OC-SVM) problem, and then discuss its use as an improved abrupt change detection method. Finally, we apply the proposed procedure to artificial and real-world examples, and demonstrate that it is competitive by comparison against other abrupt change detection methods.Fil: Grinblat, Guillermo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Uzal, Lucas César. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaElsevier2013-12-15info:eu-repo/date/embargoEnd/2016-01-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/3196Grinblat, Guillermo Luis; Uzal, Lucas César; Granitto, Pablo Miguel; Abrupt change detection with One-Class Time-Adaptive Support Vector Machines; Elsevier; Expert Systems with Applications; 40; 18; 15-12-2013; 7242-72490957-4174enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2013.06.074info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417413004739info:eu-repo/semantics/altIdentifier/issn/0957-4174info:eu-repo/semantics/embargoedAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:12:56Zoai:ri.conicet.gov.ar:11336/3196instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:12:56.942CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Abrupt change detection with One-Class Time-Adaptive Support Vector Machines
title Abrupt change detection with One-Class Time-Adaptive Support Vector Machines
spellingShingle Abrupt change detection with One-Class Time-Adaptive Support Vector Machines
Grinblat, Guillermo Luis
ABRUPT CHANGE DETECTION
ONE CLASS CLASSIFICATION
SUPPORT VECTOR MACHINE
title_short Abrupt change detection with One-Class Time-Adaptive Support Vector Machines
title_full Abrupt change detection with One-Class Time-Adaptive Support Vector Machines
title_fullStr Abrupt change detection with One-Class Time-Adaptive Support Vector Machines
title_full_unstemmed Abrupt change detection with One-Class Time-Adaptive Support Vector Machines
title_sort Abrupt change detection with One-Class Time-Adaptive Support Vector Machines
dc.creator.none.fl_str_mv Grinblat, Guillermo Luis
Uzal, Lucas César
Granitto, Pablo Miguel
author Grinblat, Guillermo Luis
author_facet Grinblat, Guillermo Luis
Uzal, Lucas César
Granitto, Pablo Miguel
author_role author
author2 Uzal, Lucas César
Granitto, Pablo Miguel
author2_role author
author
dc.subject.none.fl_str_mv ABRUPT CHANGE DETECTION
ONE CLASS CLASSIFICATION
SUPPORT VECTOR MACHINE
topic ABRUPT CHANGE DETECTION
ONE CLASS CLASSIFICATION
SUPPORT VECTOR MACHINE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We recently introduced an algorithm for training a sequence of coupled Support Vector Machines which shows promising results in the field of non-stationary classification problems Grinblat, Uzal, Ceccatto, and Granitto (2011). In this paper we analyze its application to the abrupt change detection problem. With this goal, we first introduce and analyze an extension of it to deal with the One-Class Support Vector Machine (OC-SVM) problem, and then discuss its use as an improved abrupt change detection method. Finally, we apply the proposed procedure to artificial and real-world examples, and demonstrate that it is competitive by comparison against other abrupt change detection methods.
Fil: Grinblat, Guillermo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Uzal, Lucas César. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina
description We recently introduced an algorithm for training a sequence of coupled Support Vector Machines which shows promising results in the field of non-stationary classification problems Grinblat, Uzal, Ceccatto, and Granitto (2011). In this paper we analyze its application to the abrupt change detection problem. With this goal, we first introduce and analyze an extension of it to deal with the One-Class Support Vector Machine (OC-SVM) problem, and then discuss its use as an improved abrupt change detection method. Finally, we apply the proposed procedure to artificial and real-world examples, and demonstrate that it is competitive by comparison against other abrupt change detection methods.
publishDate 2013
dc.date.none.fl_str_mv 2013-12-15
info:eu-repo/date/embargoEnd/2016-01-31
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/3196
Grinblat, Guillermo Luis; Uzal, Lucas César; Granitto, Pablo Miguel; Abrupt change detection with One-Class Time-Adaptive Support Vector Machines; Elsevier; Expert Systems with Applications; 40; 18; 15-12-2013; 7242-7249
0957-4174
url http://hdl.handle.net/11336/3196
identifier_str_mv Grinblat, Guillermo Luis; Uzal, Lucas César; Granitto, Pablo Miguel; Abrupt change detection with One-Class Time-Adaptive Support Vector Machines; Elsevier; Expert Systems with Applications; 40; 18; 15-12-2013; 7242-7249
0957-4174
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2013.06.074
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417413004739
info:eu-repo/semantics/altIdentifier/issn/0957-4174
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv embargoedAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
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:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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