Fast Support Vector Machine Classification for Large Data Sets

Abstract

AbstractNormal support vector machine (SVM) algorithms are not suitable for classification of large data sets because of high training complexity. This paper introduces a novel two-stage SVM classification approach for large data sets. Fast clustering techniques are introduced to select the training data from the original data set for the first stage SVM, and a de-clustering technique is then proposed to recover the training data for the second stage SVM. The proposed two-stage SVM classifier has distinctive advantages on dealing with huge data sets such as those in bioinformatics. Finally, we apply the proposed method on several benchmark problems. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers.