Comparison of Feature Extraction Methods and Predictors for Income Inference

Autores
Fixman, Martín; Minnoni, Martín; Sarraute, Carlos
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Abstract—Patterns of mobile phone communications, coupled with the information of the social network graph and financial behavior, allow us to make inferences of users’ socio-economic attributes such as their income level. We present here several methods to extract features from mobile phone usage (calls and messages), and compare different combinations of supervised machine learning techniques and sets of features used as input for the inference of users’ income. Our experimental results show that the Bayesian method based on the communication graph outperforms standard machine learning algorithms using nodebased features.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
Teléfono Celular
aprendizaje automático
método bayesiano
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:10915/63174
Acceder
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network_name_str SEDICI (UNLP)
spelling Comparison of Feature Extraction Methods and Predictors for Income InferenceFixman, MartínMinnoni, MartínSarraute, CarlosCiencias InformáticasTeléfono Celularaprendizaje automáticométodo bayesianoAbstract—Patterns of mobile phone communications, coupled with the information of the social network graph and financial behavior, allow us to make inferences of users’ socio-economic attributes such as their income level. We present here several methods to extract features from mobile phone usage (calls and messages), and compare different combinations of supervised machine learning techniques and sets of features used as input for the inference of users’ income. Our experimental results show that the Bayesian method based on the communication graph outperforms standard machine learning algorithms using nodebased features.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2017-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf20-22http://sedici.unlp.edu.ar/handle/10915/63174enginfo:eu-repo/semantics/altIdentifier/url/http://www.clei2017-46jaiio.sadio.org.ar/sites/default/files/Mem/AGRANDA/AGRANDA-06.pdfinfo:eu-repo/semantics/altIdentifier/issn/2451-7569info: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:UNLP2025-09-03T10:40:52Zoai:sedici.unlp.edu.ar:10915/63174Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:40:53.031SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Comparison of Feature Extraction Methods and Predictors for Income Inference
title Comparison of Feature Extraction Methods and Predictors for Income Inference
spellingShingle Comparison of Feature Extraction Methods and Predictors for Income Inference
Fixman, Martín
Ciencias Informáticas
Teléfono Celular
aprendizaje automático
método bayesiano
title_short Comparison of Feature Extraction Methods and Predictors for Income Inference
title_full Comparison of Feature Extraction Methods and Predictors for Income Inference
title_fullStr Comparison of Feature Extraction Methods and Predictors for Income Inference
title_full_unstemmed Comparison of Feature Extraction Methods and Predictors for Income Inference
title_sort Comparison of Feature Extraction Methods and Predictors for Income Inference
dc.creator.none.fl_str_mv Fixman, Martín
Minnoni, Martín
Sarraute, Carlos
author Fixman, Martín
author_facet Fixman, Martín
Minnoni, Martín
Sarraute, Carlos
author_role author
author2 Minnoni, Martín
Sarraute, Carlos
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Teléfono Celular
aprendizaje automático
método bayesiano
topic Ciencias Informáticas
Teléfono Celular
aprendizaje automático
método bayesiano
dc.description.none.fl_txt_mv Abstract—Patterns of mobile phone communications, coupled with the information of the social network graph and financial behavior, allow us to make inferences of users’ socio-economic attributes such as their income level. We present here several methods to extract features from mobile phone usage (calls and messages), and compare different combinations of supervised machine learning techniques and sets of features used as input for the inference of users’ income. Our experimental results show that the Bayesian method based on the communication graph outperforms standard machine learning algorithms using nodebased features.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description Abstract—Patterns of mobile phone communications, coupled with the information of the social network graph and financial behavior, allow us to make inferences of users’ socio-economic attributes such as their income level. We present here several methods to extract features from mobile phone usage (calls and messages), and compare different combinations of supervised machine learning techniques and sets of features used as input for the inference of users’ income. Our experimental results show that the Bayesian method based on the communication graph outperforms standard machine learning algorithms using nodebased features.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/63174
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dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/2451-7569
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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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
20-22
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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instname_str Universidad Nacional de La Plata
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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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