Extraction of decision rules via imprecise probabilities

Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via decision trees. The decision trees used by the original method were built using...

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Autores: Abellán, J., Garach, L., Castellano, Javier G., López-Maldonado, Griselda|||0000-0001-9012-0599
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/149156
Acceso en línea:https://riunet.upv.es/handle/10251/149156
Access Level:acceso abierto
Palabra clave:Imprecise probabilities
Imprecise Dirichlet model
Non-parametric predictive inference model
Uncertainty measures
Decision rules
Traffic accident severity
INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES
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repository_id_str
spelling Extraction of decision rules via imprecise probabilitiesAbellán, J.Garach, L.Castellano, Javier G.López-Maldonado, Griselda|||0000-0001-9012-0599Imprecise probabilitiesImprecise Dirichlet modelNon-parametric predictive inference modelUncertainty measuresDecision rulesTraffic accident severityINGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTESData analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via decision trees. The decision trees used by the original method were built using classic split criteria. The performance of new split criteria based on imprecise probabilities and uncertainty measures, called credal split criteria, differs significantly from the performance obtained using the classic criteria. This paper extends the IRNV method using two credal split criteria: one based on a mathematical parametric model, and other one based on a non-parametric model. The performance of the method is analyzed using a case study of traffic accident data to identify patterns related to the severity of an accident. We found that a larger number of rules is generated, significantly supplementing the information obtained using the classic split criteria.This work has been supported by the Spanish "Ministerio de Economia y Competitividad" [Project number TEC2015-69496-R] and FEDER funds.Taylor & FrancisDepartamento de Ingeniería e Infraestructura de los TransportesInstituto del Transporte y TerritorioEscuela Técnica Superior de Ingeniería de Caminos, Canales y PuertosEuropean Regional Development FundMinisterio de Economía y CompetitividadRepositorio Institucional de la Universitat Politècnica de València Riunet20172017-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/149156reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengMinisterio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 TEC2015-69496-R DESARROLLO DE HERRAMIENTAS EN LA MINERIA DE DATOS UTILIZANDO MODELOS BASADOS EN PROBABILIDADES IMPRECISAS. APLICACIONES EN PROBLEMAS DE TRAFICOopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1491562026-06-13T07:49:27Z
dc.title.none.fl_str_mv Extraction of decision rules via imprecise probabilities
title Extraction of decision rules via imprecise probabilities
spellingShingle Extraction of decision rules via imprecise probabilities
Abellán, J.
Imprecise probabilities
Imprecise Dirichlet model
Non-parametric predictive inference model
Uncertainty measures
Decision rules
Traffic accident severity
INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES
title_short Extraction of decision rules via imprecise probabilities
title_full Extraction of decision rules via imprecise probabilities
title_fullStr Extraction of decision rules via imprecise probabilities
title_full_unstemmed Extraction of decision rules via imprecise probabilities
title_sort Extraction of decision rules via imprecise probabilities
dc.creator.none.fl_str_mv Abellán, J.
Garach, L.
Castellano, Javier G.
López-Maldonado, Griselda|||0000-0001-9012-0599
author Abellán, J.
author_facet Abellán, J.
Garach, L.
Castellano, Javier G.
López-Maldonado, Griselda|||0000-0001-9012-0599
author_role author
author2 Garach, L.
Castellano, Javier G.
López-Maldonado, Griselda|||0000-0001-9012-0599
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería e Infraestructura de los Transportes
Instituto del Transporte y Territorio
Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos
European Regional Development Fund
Ministerio de Economía y Competitividad
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Imprecise probabilities
Imprecise Dirichlet model
Non-parametric predictive inference model
Uncertainty measures
Decision rules
Traffic accident severity
INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES
topic Imprecise probabilities
Imprecise Dirichlet model
Non-parametric predictive inference model
Uncertainty measures
Decision rules
Traffic accident severity
INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES
description Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via decision trees. The decision trees used by the original method were built using classic split criteria. The performance of new split criteria based on imprecise probabilities and uncertainty measures, called credal split criteria, differs significantly from the performance obtained using the classic criteria. This paper extends the IRNV method using two credal split criteria: one based on a mathematical parametric model, and other one based on a non-parametric model. The performance of the method is analyzed using a case study of traffic accident data to identify patterns related to the severity of an accident. We found that a larger number of rules is generated, significantly supplementing the information obtained using the classic split criteria.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/149156
url https://riunet.upv.es/handle/10251/149156
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 TEC2015-69496-R DESARROLLO DE HERRAMIENTAS EN LA MINERIA DE DATOS UTILIZANDO MODELOS BASADOS EN PROBABILIDADES IMPRECISAS. APLICACIONES EN PROBLEMAS DE TRAFICO
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869414109431201792
score 15.300724

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