Semi-supervised Min-Max Modular SVM

Min-Max Modular Support Vector Machine (M3-SVM) is a powerful supervised ensemble pattern classification method, and it can efficiently deal with large scale labeled data. However, it is very expensive, even infeasible, to label the large scale data set. In order to extend the M3-SVM to handle unlabeled data, a Semi-Supervised M3-SVM learning algorithm (SS-M3-SVM) is proposed in this paper. SS-M3-SVM completes the task decomposition for labeled and unlabeled data, then combines the unlabeled sample subset with labeled sample subset and explores some hidden concepts exist in this combined sample subset. After the hidden concepts explored, the posterior probability of each concept with respect to labeled samples are treated as new features for these labeled samples. Some discriminant information derived from unlabeled data is embedded in these new features. Then each base SVM classifier is trained on the labeled data subset with addition of new features. Finally, the base classifiers are combined using Min-Max rule to obtain the SS-M3-SVM. Experiments on different data sets indicate that the proposed semi-supervised learning strategy can enhance the classification performance of traditional M3-SVM.