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dc.creatorAmelec, Viloria
dc.creatorPineda Lezama, Omar Bonerge
dc.creatorVarela Izquierdo, Noel
dc.date.accessioned2020-02-03T19:28:28Z
dc.date.available2020-02-03T19:28:28Z
dc.date.issued2019
dc.identifier.issn00002010
dc.identifier.urihttp://hdl.handle.net/11323/5977
dc.description.abstractThe research proposes a new simple Bayesian classifier (SBND) with Markov from the class variable to a network structure. Experimental tests are carried out by working a dropout analysis on students enrolled in the Faculty of Engineering Sciences of Mumbai University, in India in the period 2017-2018 on the basis of socioeconomic data. The Weka tool is then used to perform the classification and the proposed model is statistically compared with other Bayesian classifiers.spa
dc.description.abstractLa investigación propone un nuevo clasificador bayesiano simple (SBND) con Markov de la variable de clase a una estructura de red. Las pruebas experimentales se llevan a cabo mediante un análisis de abandono en los estudiantes matriculados en la Facultad de Ciencias de la Ingeniería de la Universidad de Mumbai, en la India, en el período 2017-2018 sobre la base de datos socioeconómicos. La herramienta Weka se utiliza para realizar la clasificación y el modelo propuesto se compara estadísticamente con otros clasificadores bayesianos.spa
dc.language.isoengspa
dc.publisherProcedia Computer Sciencespa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectBayesian networksspa
dc.subjectBayesian classifierspa
dc.subjectEducational analysisspa
dc.subjectRedes bayesianasspa
dc.subjectClasificacion bayesianaspa
dc.subjectAnalisis educacionalspa
dc.titleBayesian classifier applied to higher education dropoutspa
dc.title.alternativeClasificador bayesiano aplicado al abandono de la educación superiorspa
dc.typeArticlespa
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dcterms.referencesOviedo, B. a. (2015). Análisis de datos educativos utilizando redes bayesianas, Latin American and Caribbean Conference for Engineering and Technology LACCEI 2015.spa
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dcterms.referencesVasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017).spa
dcterms.referencesVásquez, C., Torres-Samuel, M., Viloria, A., Lis-Gutiérrez, J.P., Crissien Borrero, T., Varela, N., Cabrera, D.: Cluster of the Latin American Universities Top100 According to Webometrics 2017. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018).spa
dcterms.referencesSevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. European Journal of Operational Research 237(1), 1095-104 (2014).spa
dcterms.referencesViloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching – Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data.spa
dcterms.referencesSoca, E.B., El trabajo independiente en el proceso de enseñanza-aprendizaje, ISSN: 1684-1859, Revista Cubana de Informática Médica, 7(2), 122-131 (2015)spa
dcterms.referencesVanyolos, E., I. Furka, I. Miko y otros tres autores. How does practice improve the skills of medical students during consecutive training courses? doi; https://dx.doi.org/10.1590/s0102-865020170060000010. Rev. Acta Cirurgica Brasileira, 32(6), 491-502 (2017)spa
dcterms.referencesIsasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson. ISBN 8420540250 (2004).spa
dcterms.referencesHaykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009).spa
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersionspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa


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