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Comparison of Predictive Models with Balanced Classes Using the SMOTE Method for the Forecast of Student Dropout in Higher Education

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Comparison of Predictive Models with Balanced Classes Using the SMOTE Method for the Forecast of Student Dropout in Higher Education

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Flores, V.; Heras, S.; Julian, V. (2022). Comparison of Predictive Models with Balanced Classes Using the SMOTE Method for the Forecast of Student Dropout in Higher Education. Electronics. 11(3):1-16. https://doi.org/10.3390/electronics11030457

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/193205

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Título: Comparison of Predictive Models with Balanced Classes Using the SMOTE Method for the Forecast of Student Dropout in Higher Education
Autor: Flores, Vaneza Heras, Stella Julian, Vicente
Entidad UPV: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Fecha difusión:
Resumen:
[EN] Based on the premise that university student dropout is a social problem in the university ecosystem of any country, technological leverage is a way that allows us to build technological proposals to solve a poorly ...[+]
Palabras clave: University student dropout , Predictive model , Data mining , SMOTE
Derechos de uso: Reconocimiento (by)
Fuente:
Electronics. (eissn: 2079-9292 )
DOI: 10.3390/electronics11030457
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/electronics11030457
Código del Proyecto:
info:eu-repo/grantAgreement/AEI//TIN2017-89156-R//AGENTES INTELIGENTES PARA ASESORAR EN PRIVACIDAD EN REDES SOCIALES/
info:eu-repo/grantAgreement/CONSELLERIA EDUCACIO//PROMETEO%2F2008%2F051//ADVANCES ON AGREEMENT TECHNOLOGIES FOR COMPUTATIONAL ENTITIES /
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2008%2F002/
Agradecimientos:
This work was partially supported by the Spanish Government project TIN2017-89156-R, and the Valencian Government project PROMETEO/2018/002. The research was developed thanks to the support of the National University of ...[+]
Tipo: Artículo

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