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Título : | Decision Tree based Classifiers for Large Datasets |
Otros títulos : | Clasificadores basados en arboles de decisión para grandes conjuntos de datos |
Autor : | Anilu, Franco-Arcega Jesús Ariel, Carrasco-Ochoa Guillermo, Sánchez-Díaz José Francisco, Martínez-Trinidad |
Palabras clave : | Keywords: Decision trees, supervised classification, large datasets. |
Fecha de publicación : | 6-mar-2013 |
Editorial : | Computación y Sistemas; Vol. 17 No. 1 |
Citación : | Computación y Sistemas; Vol. 17 No. 1 |
Citación : | Computación y Sistemas;Vol. 17 No. 1 |
Resumen : | Abstract: In this paper, several algorithms have been developed for building decision trees from large datasets. These algorithms overcome some restrictions of the most recent algorithms in the state of the art. Three of these algorithms have been designed to process datasets described exclusively by numeric attributes, and the fourth one, for processing mixed datasets. The proposed algorithms process all the training instances without storing the whole dataset in the main memory. Besides, the developed algorithms are faster than the most recent algorithms for building decision trees from large datasets, and reach competitive accuracy rates. |
URI : | http://www.repositoriodigital.ipn.mx/handle/123456789/14665 |
ISSN : | 1405-5546 |
Aparece en las colecciones: | Revistas |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
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95_Art. 9_Vol. 17 No. 1.pdf | Report on PhD Thesis | 366.15 kB | Adobe PDF | Visualizar/Abrir |
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