Information Theory based Feature Selection for Customer Classification

Autores
Barraza, Néstor Rubén; Moro, Sergio; Ferreyra, Marcelo; de la Peña, Adolfo
Año de publicación
2016
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The application of Information Theory techniques in customer feature selection is analyzed. This method, usually called information gain has been demonstrated to be simple and fast for feature selection. The important concept of mutual information, originally introduced to analyze and model a noisy channel is used in order to measure relations between characteristics of given customers. An application to a bank customers data set of telemarketing calls for selling bank long-term deposits is shown.We show that with our method, 80% of the subscribers can be reached by contacting just the better half of the classified clients.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
Segmentation
mutual information
Feature evaluation and selection
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/56974
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/56974
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Information Theory based Feature Selection for Customer ClassificationBarraza, Néstor RubénMoro, SergioFerreyra, Marcelode la Peña, AdolfoCiencias InformáticasSegmentationmutual informationFeature evaluation and selectionThe application of Information Theory techniques in customer feature selection is analyzed. This method, usually called information gain has been demonstrated to be simple and fast for feature selection. The important concept of mutual information, originally introduced to analyze and model a noisy channel is used in order to measure relations between characteristics of given customers. An application to a bank customers data set of telemarketing calls for selling bank long-term deposits is shown.We show that with our method, 80% of the subscribers can be reached by contacting just the better half of the classified clients.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2016-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1-8http://sedici.unlp.edu.ar/handle/10915/56974enginfo:eu-repo/semantics/altIdentifier/url/http://45jaiio.sadio.org.ar/sites/default/files/ASAI-07_0.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7585info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:06:13Zoai:sedici.unlp.edu.ar:10915/56974Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:06:13.205SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Information Theory based Feature Selection for Customer Classification
title Information Theory based Feature Selection for Customer Classification
spellingShingle Information Theory based Feature Selection for Customer Classification
Barraza, Néstor Rubén
Ciencias Informáticas
Segmentation
mutual information
Feature evaluation and selection
title_short Information Theory based Feature Selection for Customer Classification
title_full Information Theory based Feature Selection for Customer Classification
title_fullStr Information Theory based Feature Selection for Customer Classification
title_full_unstemmed Information Theory based Feature Selection for Customer Classification
title_sort Information Theory based Feature Selection for Customer Classification
dc.creator.none.fl_str_mv Barraza, Néstor Rubén
Moro, Sergio
Ferreyra, Marcelo
de la Peña, Adolfo
author Barraza, Néstor Rubén
author_facet Barraza, Néstor Rubén
Moro, Sergio
Ferreyra, Marcelo
de la Peña, Adolfo
author_role author
author2 Moro, Sergio
Ferreyra, Marcelo
de la Peña, Adolfo
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Segmentation
mutual information
Feature evaluation and selection
topic Ciencias Informáticas
Segmentation
mutual information
Feature evaluation and selection
dc.description.none.fl_txt_mv The application of Information Theory techniques in customer feature selection is analyzed. This method, usually called information gain has been demonstrated to be simple and fast for feature selection. The important concept of mutual information, originally introduced to analyze and model a noisy channel is used in order to measure relations between characteristics of given customers. An application to a bank customers data set of telemarketing calls for selling bank long-term deposits is shown.We show that with our method, 80% of the subscribers can be reached by contacting just the better half of the classified clients.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description The application of Information Theory techniques in customer feature selection is analyzed. This method, usually called information gain has been demonstrated to be simple and fast for feature selection. The important concept of mutual information, originally introduced to analyze and model a noisy channel is used in order to measure relations between characteristics of given customers. An application to a bank customers data set of telemarketing calls for selling bank long-term deposits is shown.We show that with our method, 80% of the subscribers can be reached by contacting just the better half of the classified clients.
publishDate 2016
dc.date.none.fl_str_mv 2016-09
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/10915/56974
url http://sedici.unlp.edu.ar/handle/10915/56974
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://45jaiio.sadio.org.ar/sites/default/files/ASAI-07_0.pdf
info:eu-repo/semantics/altIdentifier/issn/2451-7585
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
dc.format.none.fl_str_mv application/pdf
1-8
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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