Please use this identifier to cite or link to this item: http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2274
Decision tree based classifiers for large datasets
Anilú Franco Arcega
Jesús Ariel Carrasco Ochoa
GUILLERMO SANCHEZ DIAZ
José Francisco Martínez Trinidad
Acceso Abierto
Atribución-NoComercial-SinDerivadas
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.
Computación y Sistemas
2013
Artículo
Inglés
Estudiantes
Investigadores
Público en general
Franco-Arcega, A., et al., (2013). Decision tree based classifiers for large datasets, Computación y Sistemas, Vol. 15 (2): 95-102
CIENCIA DE LOS ORDENADORES
Versión aceptada
acceptedVersion - Versión aceptada
Appears in Collections:Artículos de Ciencias Computacionales

Upload archives