Descripción de metodologías de Machine Learning (ML) para la identificación de actividades a través de reconocimiento de patrones.

• Autores: Oballe Neyra, Carlos Enrique, Masias Rugel, Xiomara de los Milagros, Aldana Yarlequé, Cristhian Nicolás
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Nacional de Frontera
• Repositorio: UNF-Aypate
• Lenguaje: español
• OAI Identifier: oai:ojs2.aypate.revista.unf.edu.pe:article/77
• Enlace del recurso: https://revistas.unf.edu.pe/index.php/aypate/article/view/77
• Nivel de acceso: acceso abierto
• Materia: ML; aprendizaje semi-supervisado; clasificación; etiquetado; semi-supervised learning; classification; labelling

The application of Machine Learning (ML) models is becoming more and more frequent for the implementation, automation and systematization of processes. However, the models and techniques that are available in the literature and current development are designed with the aim of obtaining a better performance in a given problem, either to enhance the evaluation and classification of labelled data or to enhance the search for clusters or highly probable groups for the correct classification, where the former serves to improve the accuracy in the evaluation of the classification.  since it does not care about labelling, while the second serves to improve classification, considering that the data is not labelled. Due to the advantages and disadvantages presented by the efficiency of these approaches to the use of an extensive database, hybrid models are used in order to obtain the correct classification more accurately, and in particular, the present study carried out an analysis of four ML approaches of supervised learning implemented by the application of algorithms, to closely track processes and understand each issue. The results showed a variable accuracy between 30% and 50% with respect to the zero accuracy of unsupervised models. In addition, it was concluded that the developed model was conditioned for a potential improvement with the implementation of the semi-supervised Hierarchical Extreme Learning Machine (HELM) model, which is suggested to be used as a necessary complement in classic ML models for unsupervised predictions.