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 apprent...
- 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
| Summary: | 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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