Comparación de modelos de clasificación para el acceso a bonos de desarrollo humano postpandemia

Given this situation, people have the opportunity to receive a monetary incentive called "Bono de Desarrollo Humano" (Human Development Bonus). The purpose of this paper is to contribute to the development of a model that classifies those who will benefit from this bonus through a performa...

Full description

Saved in:
Bibliographic Details
Published in:RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação 2024-09 (E73), p.532-545
Main Authors: Alejandro-López, Henry, Marcelo-Barragán, Bryan
Format: Article
Language:Spanish
Subjects:
Accuracy
Algorithms
Classification
Human performance
Machine learning
Pandemics
Support vector machines
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Given this situation, people have the opportunity to receive a monetary incentive called "Bono de Desarrollo Humano" (Human Development Bonus). The purpose of this paper is to contribute to the development of a model that classifies those who will benefit from this bonus through a performance comparison. The results favor the SVM algorithm that obtains a higher accuracy (0.9469) with respect to the XG Boosting version (0.90), however, in the execution time metric the XG Boosting is more efficient with 0.49 seconds versus 14.98 minutes for the SVM algorithm. The final conclusion is that the best model for bond classification is XG Boosting, presenting outstanding accuracy versus execution time.
ISSN:1646-9895