Multi-Class Support Vector Machines for Large Data Sets via Minimum Enclosing Ball Clustering

Support vector machines (SVM) for binary classification have been developed in a broad field of applications. But normal 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: minimum enclosing ball (MEB) clustering is 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. Then we extend binary SVM classification to case of multiclass. The novel two-stage multi-class SVM has distinctive advantages on dealing with huge data sets. Finally, we apply the proposed method on several benchmark problems, experimental results demonstrate that our approach have good classification accuracy while the training is significantly faster than other SVM classifiers.