Data selection based on decision tree for SVM classification on large data sets

dc.creatorCervantes Canales, Jair; 101829
dc.creatorGarcía Lamont, Farid; 216477
dc.creatorLOPEZ CHAU, ASDRUBAL; 100664
dc.creatorRodríguez Mazahua, Lisbeth; 268183
dc.creatorRUIZ CASTILLA, JOSE SERGIO; 231221
dc.creatorCervantes Canales, Jair
dc.creatorGarcía Lamont, Farid
dc.creatorLOPEZ CHAU, ASDRUBAL
dc.creatorRodríguez Mazahua, Lisbeth
dc.creatorRUIZ CASTILLA, JOSE SERGIO
dc.date2016-05-11T15:46:15Z
dc.date2016-05-11T15:46:15Z
dc.date2015-08-18
dc.identifier1568-4946
dc.identifierhttp://hdl.handle.net/20.500.11799/41184
dc.descriptionSupport Vector Machine (SVM) has important properties such as a strong mathematical background and a better generalization capability with respect to other classification methods. On the other hand, the major drawback of SVM occurs in its training phase, which is computationally expensive and highly dependent on the size of input data set. In this study, a new algorithm to speed up the training time of SVM is presented; this method selects a small and representative amount of data from data sets to improve training time of SVM. The novel method uses an induction tree to reduce the training data set for SVM, producing a very fast and high-accuracy algorithm. According to the results, the proposed algorithm produces results with similar accuracy and in a faster way than the current SVM implementations.
dc.descriptionProyecto UAEM 3771/2014/CI
dc.languageeng
dc.publisherApplied Soft Computing
dc.relationdx.doi.org/10.1016/j.asoc.2015.08.048;
dc.rightsopenAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectSVM
dc.subjectClassification
dc.subjectLarge data sets
dc.subjectINGENIERÍA Y TECNOLOGÍA
dc.titleData selection based on decision tree for SVM classification on large data sets
dc.typeArtículos de revistas
dc.typeArtículos de revistas


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