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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/196991
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:35:31Zoai: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:34982025-09-29 09:35:32.073CONICET 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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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1844613107215761408 |
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13.070432 |