Reducing the number of sub-classifiers for pairwise multi-category support vector machines

Among the SVM-based methods for multi-category classification, “1-a-r”, pairwise and DAGSVM are most widely used. The deficiency of “1-a-r” is long training time and unclassifiable region; the deficiency of pairwise and DAGSVM is the redundancy of sub-classifiers. We propose an uncertainty sampling-based multi-category SVM in this paper. In the new method, some necessary sub-classifiers instead of all N × (N − 1)/2 sub-classifiers are selected to be trained and the uncertainty sampling strategy is used to decide which samples should be selected in each training round. This uncertainty sampling-based method is proved to be accurate and efficient by experimental results on the benchmark data.