Combining diverse one-class classifiers by means of dynamic weighted average for multi-class pattern classification

摘要

One-Class Classifier (OCC) has been widely used for its ability to learn without counterexamples. Its extension for multi-class implementation offers an open scheme which allows easily adding new classes. However, using OCCs for the multi-class implementation usually achieves less accuracy than the usual multi-class implementations. Hence, in order to improve the accuracy and keep the offered advantage, the suitable approach consists to combine different classifiers. Thus, this paper proposes a study of combining different types of OCC for multi-class classification by means of a new Dynamic Weighted Average (DWA) combination rule. Experimental results conducted on several real-world datasets prove the effective use of the proposed approach where the DWA rule achieves the best results against fixed rules as well as the decision template. Furthermore, comparison of the proposed open classification system against a standard open classifier based on K-Nearest Neighbor (K-NN) shows the superiority of the proposed system.