Ensemble One-class Classifiers Based on Hybrid Diversity Generation and Pruning

Combining one-class classifiers using the classical ensemble methods is not satisfactory. To address this problem, this paper first proves that though one-class classification performance can be improved by a classifier ensemble, it can also degrade if the set of base classifiers are not selected carefully. On this basis, this study further analyzes that the lacking of diversity heavily accounts for performance degradation. Therefore, a hybrid method for generating diverse base classifiers is proposed. Secondly, in the combining phase, to find the most useful diversity, the one-class ensemble loss is split and analyzed theoretically to propose a diversity based pruning method. Finally, by combining these two steps, a novel ensemble one-class classifier named Pruned Hybrid Diverse Ensemble One-class Classifier (PHD-EOC) is proposed. The experimental results on the UCI datasets and a malicious software detection dataset show that the PHD-EOC strikes a better balance between the diverse base classifiers and classification performance. It also outperforms other classical ensemble methods for a faster decision speed.