A fast SVM training algorithm based on a decision tree data filter

Authors:
Jair Cervantes;Asdrúbal López;Farid García;Adrián Trueba
Affiliations:
UAEM-Texcoco, Autonomous University of Mexico State, México;Instituto Politécnico Nacional 2508, Center of Research and Advanced Studies-IPN, México DF;Autonomous University of Hidalgo State, Tizayuca-Hidalgo, México;UAEM-Texcoco, Autonomous University of Mexico State, México
Venue:
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Year:
2011

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Abstract

In this paper we present a new algorithm to speed up the training time of Support Vector Machines (SVM). SVM has some important properties like solid mathematical background and a better generalization capability than other machines like for example neural networks. 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. The proposed algorithm uses a data filter to reduce the input data set to train a SVM. The data filter is based on an induction tree which effectively reduces the training data set for SVM, producing a very fast and high accuracy algorithm. According to the results, the algorithm produces results in a faster way than existing SVM implementations (SMO, LIBSVM and Simple-SVM) with similar accurateness.