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
Materias
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
Recurso de información
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