A New SVM Reduction Strategy of Large-Scale Training Sample Sets

Fang Zhu, Junfang Wei and Tao Gao
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Fang Zhu: School of Computer and Communication Engineering, Northeastern University, Qinhuangdao, China
Junfang Wei: School of Resource and Material, Northeastern University, Qinhuangdao, China & Tianjin Foreign Studies University, TianJin, China
Tao Gao: North China Electric Power University, Beijing, China & Electronic Information Products Supervision and Inspection Institute of Hebei Province, ShijiaZhuang, China

International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), 2012, vol. 4, issue 4, 63-73

Abstract: There has become a bottleneck to use support vector machine (SVM) due to the problems such as slow learning speed, large buffer memory requirement, low generalization performance and so on. These problems are caused by large-scale training sample set and outlier data immixed in the other class. Aiming at these problems, this paper proposed a new reduction strategy for large-scale training sample set according to analyzing on the structure of the training sample set based on the point set theory. By using fuzzy clustering method in this new strategy, the potential support vectors are obtained and the non-boundary outlier data immixed in the other class is removed. In view of reducing greatly the scale of the training sample set, it improves the generalization performance of SVM and effectively avoids over-learning. Finally, the experimental results shown the given reduction strategy can not only reduce the train samples of SVM and speed up the train process, but also ensure accuracy of classification.

Date: 2012
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