Ponencia en Congreso:
Prediction in health domain using Bayesian networks optimization based on induction learning techniques

dc.contributor.authorFelgaer, Pablo
dc.contributor.authorBritos, Paola Verónica
dc.contributor.authorGarcía Martínez, Ramón
dc.date.accessioned2019-01-24T15:58:01Z
dc.date.available2019-01-24T15:58:01Z
dc.date.issued2006
dc.description.abstract"A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and exible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain."en
dc.identifier.issn0129-1831
dc.identifier.urihttp://ri.itba.edu.ar/handle/123456789/1432
dc.language.isoenen
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1142/S0129183106008558
dc.subjectTEORIA BAYESIANA DE DECISIONES ESTADISTICASes
dc.subjectAPRENDIZAJEes
dc.titlePrediction in health domain using Bayesian networks optimization based on induction learning techniquesen
dc.typePonencias en Congresoses
dc.typeinfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePonencia en Congreso
itba.description.filiationFil: Felgaer, Pablo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina.
itba.description.filiationFil: Britos, Paola Verónica. Instituto Tecnológico de Buenos Aires; Argentina.
itba.description.filiationFil: García Martínez, Ramón. Instituto Tecnológico de Buenos Aires; Argentina.
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