Por favor, use este identificador para citar o enlazar este ítem: http://repositorio.ugto.mx/handle/20.500.12059/10482
Título: Híper Heurísticas para la Selección de Métodos de Aprendizaje Profundo en la Clasificación de Textos Automatizada
Autor: Jonathan Estrella Ramirez
Resumen: In this paper, an evolutionary model, in the scope of automated machine learning, that learns selection hyper-heuristics for text classification is presented. A hyper-heuristic is a set of if-then rules that evaluate a set of meta-features, summarizing the data distribution of a dataset, to select the most adequate deep learning method for such a dataset. It is expected that datasets with similar distributions can use the same classification model, generalizing the selection process. The model initially creates a population of hyper-heuristics at random and then evolves them using specific mutation and crossover operators. During the evolution, each hyper-heuristic is evaluated for its classification performance with a training group of datasets. At the end of the evolution, the best hyper-heuristic is chosen and evaluated for classification with an independent group of datasets. The results indicate that the best hyper-heuristic generalizes well the selection process, by choosing adequate classification methods for the datasets; and reaches a better performance than two state-of-the-art automated machine learning systems.
Fecha de publicación: 10-ene-2024
Editorial: Universidad de Guanajuato
Licencia: http://creativecommons.org/licenses/by-nc-nd/4.0
URI: http://repositorio.ugto.mx/handle/20.500.12059/10482
Idioma: eng
Aparece en las colecciones:Revista Jóvenes en la Ciencia



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