A Fast SVM Training Algorithm Based on a Decision Tree Data Filter

Cervantes, Jair; López, Asdrúbal; García, Farid; Trueba, Adrián

doi:10.1007/978-3-642-25324-9_16

Cited by 6 publications

(1 citation statement)

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“…The SVM is trained again, but this time with those examples selected by the DT. In [17,18], also DT is used to select examples close to decision boundary, however, the authors does not take in consideration how to optimize the sigma parameter, and the use of the algorithm on imbalanced data sets.…”

Section: Q6mentioning

confidence: 98%

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

Cervantes¹,

García-Lamont²,

López-Chau³

et al. 2015

Applied Soft Computing

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Please cite this article in press as: J. Cervantes, et al., Data selection based on decision tree for SVM classification on large data sets, Appl. Soft Comput. J. (2015), http://dx.a b s t r a c t 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.

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Section: Q6mentioning

confidence: 98%