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Extraction of decision rules via imprecise probabilities

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Extraction of decision rules via imprecise probabilities

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dc.contributor.author Abellán, J. es_ES
dc.contributor.author López-Maldonado, Griselda es_ES
dc.contributor.author Garach, L. es_ES
dc.contributor.author Castellano, Javier G. es_ES
dc.date.accessioned 2020-08-01T03:30:46Z
dc.date.available 2020-08-01T03:30:46Z
dc.date.issued 2017 es_ES
dc.identifier.issn 0308-1079 es_ES
dc.identifier.uri http://hdl.handle.net/10251/149156
dc.description "This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of General Systems on 2017, available online: https://www.tandfonline.com/doi/full/10.1080/03081079.2017.1312359" es_ES
dc.description.abstract 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. es_ES
dc.description.sponsorship This work has been supported by the Spanish "Ministerio de Economia y Competitividad" [Project number TEC2015-69496-R] and FEDER funds. es_ES
dc.language Inglés es_ES
dc.publisher Taylor & Francis es_ES
dc.relation MINECO/TEC2015-69496-R es_ES
dc.relation.ispartof International Journal of General Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Imprecise probabilities es_ES
dc.subject Imprecise Dirichlet model es_ES
dc.subject Non-parametric predictive inference model es_ES
dc.subject Uncertainty measures es_ES
dc.subject Decision rules es_ES
dc.subject Traffic accident severity es_ES
dc.subject.classification INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES es_ES
dc.title Extraction of decision rules via imprecise probabilities es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1080/03081079.2017.1312359 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería e Infraestructura de los Transportes - Departament d'Enginyeria i Infraestructura dels Transports es_ES
dc.description.bibliographicCitation Abellán, J.; López-Maldonado, G.; Garach, L.; Castellano, JG. (2017). Extraction of decision rules via imprecise probabilities. International Journal of General Systems. 46(4):313-331. https://doi.org/10.1080/03081079.2017.1312359 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1080/03081079.2017.1312359 es_ES
dc.description.upvformatpinicio 313 es_ES
dc.description.upvformatpfin 331 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 46 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela 336604 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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