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http://repositorio.ugto.mx/handle/20.500.12059/10482
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DC Field | Value | Language |
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dc.rights.license | http://creativecommons.org/licenses/by-nc-nd/4.0 | es_MX |
dc.creator | Jonathan Estrella Ramirez | es_MX |
dc.date.accessioned | 2024-03-19T16:45:53Z | - |
dc.date.available | 2024-03-19T16:45:53Z | - |
dc.date.issued | 2024-01-10 | - |
dc.identifier.uri | http://repositorio.ugto.mx/handle/20.500.12059/10482 | - |
dc.description.abstract | 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. | es_MX |
dc.language.iso | eng | es_MX |
dc.publisher | Universidad de Guanajuato | es_MX |
dc.relation | https://www.jovenesenlaciencia.ugto.mx/index.php/jovenesenlaciencia/article/view/4213 | es_MX |
dc.rights | info:eu-repo/semantics/openAccess | es_MX |
dc.source | Jóvenes en la Ciencia: Congreso Internacional de electrónica y cómputo aplicado 2023, Vol. 25 (2024) | es_MX |
dc.title | Híper Heurísticas para la Selección de Métodos de Aprendizaje Profundo en la Clasificación de Textos Automatizada | es_MX |
dc.title.alternative | Hyper-Heuristics for Selecting Deep Learning Methods in Automated Text Classification | en |
dc.type | info:eu-repo/semantics/article | es_MX |
dc.subject.cti | info:eu-repo/classification/cti/7 | es_MX |
dc.subject.keywords | Automated machine learning | en |
dc.subject.keywords | Evolutionary algorithms | en |
dc.subject.keywords | Hyper-heuristics | en |
dc.subject.keywords | Text classification | en |
dc.subject.keywords | Aprendizaje automático de máquinas | es_MX |
dc.subject.keywords | Algoritmos evolutivos | es_MX |
dc.subject.keywords | Hiperheurística | es_MX |
dc.subject.keywords | Clasificación de textos | es_MX |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_MX |
dc.creator.two | Juan Carlos Gomez | es_MX |
Appears in Collections: | Revista Jóvenes en la Ciencia |
Files in This Item:
File | Description | Size | Format | |
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Híper Heurísticas para la Selección de Métodos de Aprendizaje Profundo en la Clasificación de Textos Automatizada.pdf | 695.72 kB | Adobe PDF | View/Open |
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