Improving the k-NN method: rough set in edit training set

Autores
Caballero, Yailé; Bello, Rafael; Álvarez, Delia; García, María M.; Pizano, Yaimara
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Rough Set Theory (RST) is a technique for data analysis. In this study, we use RST to improve the performance of k-NN method. The RST is used to edit and reduce the training set. We propose two methods to edit training sets, which are based on the lower and upper approximations. Experimental results show a satisfactory performance of k-NN method using these techniques.
Applications in Artificial Intelligence - Learning and Neural Nets
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
data analysis
training sets
Rough Set Theory (RST)
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/24150
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/24150
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Improving the k-NN method: rough set in edit training setCaballero, YailéBello, RafaelÁlvarez, DeliaGarcía, María M.Pizano, YaimaraCiencias Informáticasdata analysistraining setsRough Set Theory (RST)Rough Set Theory (RST) is a technique for data analysis. In this study, we use RST to improve the performance of k-NN method. The RST is used to edit and reduce the training set. We propose two methods to edit training sets, which are based on the lower and upper approximations. Experimental results show a satisfactory performance of k-NN method using these techniques.Applications in Artificial Intelligence - Learning and Neural NetsRed de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/24150enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34655-4info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:45Zoai:sedici.unlp.edu.ar:10915/24150Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:45.472SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Improving the k-NN method: rough set in edit training set
title Improving the k-NN method: rough set in edit training set
spellingShingle Improving the k-NN method: rough set in edit training set
Caballero, Yailé
Ciencias Informáticas
data analysis
training sets
Rough Set Theory (RST)
title_short Improving the k-NN method: rough set in edit training set
title_full Improving the k-NN method: rough set in edit training set
title_fullStr Improving the k-NN method: rough set in edit training set
title_full_unstemmed Improving the k-NN method: rough set in edit training set
title_sort Improving the k-NN method: rough set in edit training set
dc.creator.none.fl_str_mv Caballero, Yailé
Bello, Rafael
Álvarez, Delia
García, María M.
Pizano, Yaimara
author Caballero, Yailé
author_facet Caballero, Yailé
Bello, Rafael
Álvarez, Delia
García, María M.
Pizano, Yaimara
author_role author
author2 Bello, Rafael
Álvarez, Delia
García, María M.
Pizano, Yaimara
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
data analysis
training sets
Rough Set Theory (RST)
topic Ciencias Informáticas
data analysis
training sets
Rough Set Theory (RST)
dc.description.none.fl_txt_mv Rough Set Theory (RST) is a technique for data analysis. In this study, we use RST to improve the performance of k-NN method. The RST is used to edit and reduce the training set. We propose two methods to edit training sets, which are based on the lower and upper approximations. Experimental results show a satisfactory performance of k-NN method using these techniques.
Applications in Artificial Intelligence - Learning and Neural Nets
Red de Universidades con Carreras en Informática (RedUNCI)
description Rough Set Theory (RST) is a technique for data analysis. In this study, we use RST to improve the performance of k-NN method. The RST is used to edit and reduce the training set. We propose two methods to edit training sets, which are based on the lower and upper approximations. Experimental results show a satisfactory performance of k-NN method using these techniques.
publishDate 2006
dc.date.none.fl_str_mv 2006-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/24150
url http://sedici.unlp.edu.ar/handle/10915/24150
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/0-387-34655-4
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.070432

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