Multiple One-Class Classifier Combination for Multi-class Classification

The One-Class Classifier (OCC) has been widely used for solving the one-class and multi-class classification problems. Its main advantage for multi-class is offering an open system and therefore allows easily extending new classes without retraining OCCs. However, extending the OCC to the multi-class classification achieves less accuracy comparatively to other multi-class classifiers. Hence, in order to improve the accuracy and keep the offered advantage we propose in this paper a Multiple Classifier System (MCS), which is composed of different types of OCC. Usually, the combination is performed using fixed or trained rules. Generally, the static weighted average is considered as straightforward combination rule. In this paper we propose a dynamic weighted average rule that calculates the appropriate weights for each test sample. Experimental results conducted on several real-world datasets proves the effective use of the proposed multiple classifier system where the dynamic weighted average rule achieves the best results for most datasets versus the mean, max, product and the static weighted average rules.