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dc.contributor.authorBeltrán Pascual M.
dc.contributor.authorMuñoz Martínez A.
dc.contributor.authorMuñoz Alamillos T.
dc.date.accessioned2020-09-02T22:13:04Z
dc.date.available2020-09-02T22:13:04Z
dc.date.issued2014
dc.identifier10.1016/j.cesjef.2013.07.001
dc.identifier.citation37, 104, 73-86
dc.identifier.issn02100266
dc.identifier.urihttps://hdl.handle.net/20.500.12728/3724
dc.descriptionThis paper analyses how to build an efficient classifier across Bayesians networks used in data mining. The purpose of using the Bayesian model is to improve credit scoring accuracy. The Bayesian approach, based on probability models, analyses risk by using the decision theory, yielding as a solution that action that maximizes the expected utility. Expert assessment may be included in the model. To show the superiority of the Bayesian approach, results obtained for real bank data are compared with those obtained with alternative parametric and non-parametric models. © 2013 Asociación Cuadernos de Economía.
dc.language.isoes
dc.publisherAsociacion Cuadernos de Economia
dc.subjectBayesians networks
dc.subjectCredit scoring
dc.subjectMarkov blanket
dc.subjectMulticlassifiers
dc.subjectROC curve
dc.titleBayesian networks applied to credit scoring problems. A practical application [Redes bayesianas aplicadas a problemas de credit scoring. Una aplicación práctica]
dc.typeArticle


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