Employing One-Class SVM Classifier Ensemble for Imbalanced Data Stream Classification

The classification of imbalanced data streams is gaining more and more interest. However, apart from the problem that one of the class is not well represented, there are problems typical for data stream classification, such as limited resources, lack of access to the true labels and the possibility of occurrence of the concept drift. Possibility of concept drift appearing enforces design in the method adaptation mechanism. In this article, we propose the OCEIS classifier (One-Class support vector machine classifier Ensemble for Imbalanced data Stream). The main idea is to supply the committee with one-class classifiers trained on clustered data for each class separately. The results obtained from experiments carried out on synthetic and real data show that the proposed method achieves results at a similar level as the state of the art methods compared with it.