dc.creatorAnilú Franco Arcega
dc.creatorJesús Ariel Carrasco Ochoa
dc.creatorGUILLERMO SANCHEZ DIAZ
dc.creatorJosé Francisco Martínez Trinidad
dc.date2013
dc.date.accessioned2023-07-25T16:25:18Z
dc.date.available2023-07-25T16:25:18Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2274
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7807454
dc.descriptionIn 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.
dc.formatapplication/pdf
dc.languageeng
dc.publisherComputación y Sistemas
dc.relationcitation:Franco-Arcega, A., et al., (2013). Decision tree based classifiers for large datasets, Computación y Sistemas, Vol. 15 (2): 95-102
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/12
dc.subjectinfo:eu-repo/classification/cti/1203
dc.subjectinfo:eu-repo/classification/cti/1203
dc.titleDecision tree based classifiers for large datasets
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


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