Optimize the Large Scale SVM Social Spam Detection Model via Bistratal Reduction Method
Xi Qin, SU Yi-dan · 2010
Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters remained and saves them as the final result set. In the traditional reduction method, the grain-size is single. There is still a contradiction between compression and accuracy, and can't be solved perfectly. Bistratal reduction method changes the reduced intensity according to the number of redundant points remained. The experiments show that bistratal reduction method gives a higher compression ratio and accuracy. Apply the new method to the large scale SVM social spam detection model. The detection model speed up obviously.