Please use this identifier to cite or link to this item: http://repositorio.ugto.mx/handle/20.500.12059/10482
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dc.rights.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0es_MX
dc.creatorJonathan Estrella Ramirezes_MX
dc.date.accessioned2024-03-19T16:45:53Z-
dc.date.available2024-03-19T16:45:53Z-
dc.date.issued2024-01-10-
dc.identifier.urihttp://repositorio.ugto.mx/handle/20.500.12059/10482-
dc.description.abstractIn 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.es_MX
dc.language.isoenges_MX
dc.publisherUniversidad de Guanajuatoes_MX
dc.relationhttps://www.jovenesenlaciencia.ugto.mx/index.php/jovenesenlaciencia/article/view/4213es_MX
dc.rightsinfo:eu-repo/semantics/openAccesses_MX
dc.sourceJóvenes en la Ciencia: Congreso Internacional de electrónica y cómputo aplicado 2023, Vol. 25 (2024)es_MX
dc.titleHíper Heurísticas para la Selección de Métodos de Aprendizaje Profundo en la Clasificación de Textos Automatizadaes_MX
dc.title.alternativeHyper-Heuristics for Selecting Deep Learning Methods in Automated Text Classificationen
dc.typeinfo:eu-repo/semantics/articlees_MX
dc.subject.ctiinfo:eu-repo/classification/cti/7es_MX
dc.subject.keywordsAutomated machine learningen
dc.subject.keywordsEvolutionary algorithmsen
dc.subject.keywordsHyper-heuristicsen
dc.subject.keywordsText classificationen
dc.subject.keywordsAprendizaje automático de máquinases_MX
dc.subject.keywordsAlgoritmos evolutivoses_MX
dc.subject.keywordsHiperheurísticaes_MX
dc.subject.keywordsClasificación de textoses_MX
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_MX
dc.creator.twoJuan Carlos Gomezes_MX
Appears in Collections:Revista Jóvenes en la Ciencia



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