Título

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

Autor

Jair Cervantes Canales

Farid García Lamont

ASDRUBAL LOPEZ CHAU

Lisbeth Rodríguez Mazahua

JOSE SERGIO RUIZ CASTILLA

Nivel de Acceso

Acceso Abierto

Resumen o descripción

Support 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.

Proyecto UAEM 3771/2014/CI

Editor

Applied Soft Computing

Fecha de publicación

18 de agosto de 2015

Tipo de publicación

Artículo

Fuente

1568-4946

Idioma

Inglés

Relación

dx.doi.org/10.1016/j.asoc.2015.08.048;

Audiencia

Estudiantes

Investigadores

Repositorio Orígen

REPOSITORIO INSTITUCIONAL DE LA UAEM

Descargas

695

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