Rule Extraction on Numeric Datasets Using Hyper-rectangles

Autores
Hasperué, Waldo; Lanzarini, Laura Cristina; de Giusti, Armando Eduardo
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, a new classification strategy is presented that uses hyper-rectangles as data descriptors to achieve a model that allows extracting knowledge in the form of classification rules. The participation of an expert for training the model is discussed. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.
Fil: Hasperué, Waldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina
Fil: Lanzarini, Laura Cristina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina
Fil: de Giusti, Armando Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina
Materia
Rule extraction
Classification
Numeric datasets
Large datasets
Hyper-rectangles
Supervised learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/196991
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Rule Extraction on Numeric Datasets Using Hyper-rectanglesHasperué, WaldoLanzarini, Laura Cristinade Giusti, Armando EduardoRule extractionClassificationNumeric datasetsLarge datasetsHyper-rectanglesSupervised learninghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, a new classification strategy is presented that uses hyper-rectangles as data descriptors to achieve a model that allows extracting knowledge in the form of classification rules. The participation of an expert for training the model is discussed. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.Fil: Hasperué, Waldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; ArgentinaFil: Lanzarini, Laura Cristina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; ArgentinaFil: de Giusti, Armando Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; ArgentinaCanadian Center of Science and Education2012-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/196991Hasperué, Waldo; Lanzarini, Laura Cristina; de Giusti, Armando Eduardo; Rule Extraction on Numeric Datasets Using Hyper-rectangles; Canadian Center of Science and Education; Computer and Information Science; 5; 4; 6-2012; 116-1311913-89891913-8997CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://ccsenet.org/journal/index.php/cis/article/view/15766info:eu-repo/semantics/altIdentifier/doi/10.5539/cis.v5n4p116info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:24:02Zoai:ri.conicet.gov.ar:11336/196991instacron: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:34982025-10-15 14:24:03.087CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Rule Extraction on Numeric Datasets Using Hyper-rectangles
title Rule Extraction on Numeric Datasets Using Hyper-rectangles
spellingShingle Rule Extraction on Numeric Datasets Using Hyper-rectangles
Hasperué, Waldo
Rule extraction
Classification
Numeric datasets
Large datasets
Hyper-rectangles
Supervised learning
title_short Rule Extraction on Numeric Datasets Using Hyper-rectangles
title_full Rule Extraction on Numeric Datasets Using Hyper-rectangles
title_fullStr Rule Extraction on Numeric Datasets Using Hyper-rectangles
title_full_unstemmed Rule Extraction on Numeric Datasets Using Hyper-rectangles
title_sort Rule Extraction on Numeric Datasets Using Hyper-rectangles
dc.creator.none.fl_str_mv Hasperué, Waldo
Lanzarini, Laura Cristina
de Giusti, Armando Eduardo
author Hasperué, Waldo
author_facet Hasperué, Waldo
Lanzarini, Laura Cristina
de Giusti, Armando Eduardo
author_role author
author2 Lanzarini, Laura Cristina
de Giusti, Armando Eduardo
author2_role author
author
dc.subject.none.fl_str_mv Rule extraction
Classification
Numeric datasets
Large datasets
Hyper-rectangles
Supervised learning
topic Rule extraction
Classification
Numeric datasets
Large datasets
Hyper-rectangles
Supervised learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, a new classification strategy is presented that uses hyper-rectangles as data descriptors to achieve a model that allows extracting knowledge in the form of classification rules. The participation of an expert for training the model is discussed. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.
Fil: Hasperué, Waldo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina
Fil: Lanzarini, Laura Cristina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina
Fil: de Giusti, Armando Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina
description When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, a new classification strategy is presented that uses hyper-rectangles as data descriptors to achieve a model that allows extracting knowledge in the form of classification rules. The participation of an expert for training the model is discussed. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.
publishDate 2012
dc.date.none.fl_str_mv 2012-06
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/11336/196991
Hasperué, Waldo; Lanzarini, Laura Cristina; de Giusti, Armando Eduardo; Rule Extraction on Numeric Datasets Using Hyper-rectangles; Canadian Center of Science and Education; Computer and Information Science; 5; 4; 6-2012; 116-131
1913-8989
1913-8997
CONICET Digital
CONICET
url http://hdl.handle.net/11336/196991
identifier_str_mv Hasperué, Waldo; Lanzarini, Laura Cristina; de Giusti, Armando Eduardo; Rule Extraction on Numeric Datasets Using Hyper-rectangles; Canadian Center of Science and Education; Computer and Information Science; 5; 4; 6-2012; 116-131
1913-8989
1913-8997
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ccsenet.org/journal/index.php/cis/article/view/15766
info:eu-repo/semantics/altIdentifier/doi/10.5539/cis.v5n4p116
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Canadian Center of Science and Education
publisher.none.fl_str_mv Canadian Center of Science and Education
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
_version_ 1846082657610366976
score 13.22299

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