ERCR TV: Ensemble of Random Committee and Random Tree for Efficient Anomaly Classification Using Voting

Anomaly Detection is widely used in applications related but not limited to intrusion detection, fault detection, fraud detection, health monitoring systems and many other places. The overall efficiency of these applications depends on the classification algorithm that is chosen. An efficient classification algorithm can thus greatly improve the accuracy of these applications. This paper proposes a hybrid approach involving Random Committee and Random Tree techniques for anomaly classification, resulting in most encouraging accuracy values. The proposed scheme is followed after the preprocessing phase that involves Feature Selection using Correlated Feature Selection (CFS) algorithm with the Best-First Search technique.