Information Theory based Feature Selection for Customer Classification

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...

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Autores principales: Barraza, Néstor Rubén, Moro, Sergio, Ferreyra, Marcelo, de la Peña, Adolfo
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2016
Materias:
Ciencias Informáticas
Segmentation
mutual information
Feature evaluation and selection
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/56974
http://45jaiio.sadio.org.ar/sites/default/files/ASAI-07_0.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-56974
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Segmentation
mutual information
Feature evaluation and selection
spellingShingle Ciencias Informáticas
Segmentation
mutual information
Feature evaluation and selection
Barraza, Néstor Rubén
Moro, Sergio
Ferreyra, Marcelo
de la Peña, Adolfo
Information Theory based Feature Selection for Customer Classification
topic_facet Ciencias Informáticas
Segmentation
mutual information
Feature evaluation and selection
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.
format Objeto de conferencia
Objeto de conferencia
author Barraza, Néstor Rubén
Moro, Sergio
Ferreyra, Marcelo
de la Peña, Adolfo
author_facet Barraza, Néstor Rubén
Moro, Sergio
Ferreyra, Marcelo
de la Peña, Adolfo
author_sort Barraza, Néstor Rubén
title Information Theory based Feature Selection for Customer Classification
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
publishDate 2016
url http://sedici.unlp.edu.ar/handle/10915/56974
http://45jaiio.sadio.org.ar/sites/default/files/ASAI-07_0.pdf
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AT morosergio informationtheorybasedfeatureselectionforcustomerclassification
AT ferreyramarcelo informationtheorybasedfeatureselectionforcustomerclassification
AT delapenaadolfo informationtheorybasedfeatureselectionforcustomerclassification
bdutipo_str Repositorios
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