Assembly of classifiers to determine the academic profile of students

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.

• Autores: Silva, Jesus; Rojas Plasencia, Karina Milagros; Senior Naveda, Alexa; Barrios, Rosio; Vargas Mercado, Carlos; Medina, Claudia
• Tipo de recurso: Article of journal
• Fecha de publicación: 2020
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/7790
• Acceso en línea: https://hdl.handle.net/11323/7790; https://doi.org/10.1016/j.procs.2020.03.102; https://repositorio.cuc.edu.co/
• Palabra clave: Assembly of classifiers; decision trees; artificial neural network
• Rights: openAccess
• License: Attribution-NonCommercial-NoDerivatives 4.0 International