Wagging for Combining Weighted One-class Support Vector Machines

Most of machine learning problems assume, that we have at our disposal objects originating from two or more classes. By learning from a representative training set a classifier is able to estimate proper decision boundaries. However, in many real-life problems obtaining objects from some of the classes is difficult, or even impossible. In such cases, we are dealing with one- class classification, or learning in the absence of counterexamples. Such recognition systems must display a high robustness to new, unseen objects that may belong to an unknown class. That is why ensemble learning has become an attractive perspective in this field. In our work, we propose a novel one-class ensemble classifier, based on wagging. A weighted version of boosting is used, and the output weights for each object are used directly in the process of training Weighted One-Class Support Vector Machines. This introduces a diversity into the pool of one-class classifiers and extends the competence of formed ensemble. Experimental analysis, carried out on a number of benchmarks and backed-up with statistical analysis proves that the proposed method can outperform state-of-the-art ensembles dedicated to one-class classification