Assembly of classifiers to determine the academic profile of students

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Date
2020
Author
Silva, Jesus
Rojas Plasencia, Karina Milagros
Senior Naveda, Alexa
Barrios, Rosio
Vargas Mercado, Carlos
Medina, Claudia
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URI: https://hdl.handle.net/11323/7790
Citar con DOI: https://doi.org/10.1016/j.procs.2020.03.102

Enlace externo del documento: https://www.sciencedirect.com/science/article/pii/S1877050920305408#!

Abstract

The assembly methods, or combination of models, arise with the purpose of improving the accuracy of predictions. An assembly contains a number of apprentices (base models) which, when of the same type are called homogeneous and if of different, heterogeneous. The characteristic is that these apprentices do not perform well. The assembly is generated using another algorithm that combines the apprentices, examples of which are the majority vote, the decision table and the neural networks [1]. This article proposes the use of an assembly of classifiers to determine the academic profile of the student, based on the student’s overall average and data related to educational factors.
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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International