A novel concept drift detection method in data streams using ensemble classifiers

Concept drift, change in the underlying distribution that data points come from, is an inevitable phenomenon in data streams. Due to increase in the number of data streams' applications such as network intrusion detection, weather forecasting, and detection of unconventional behavior in financial transactions; numerous researches have recently been conducted in the area of concept drift detection. An ideal method for concept drift detection should be able to rapidly and correctly identify changes in the underlying distribution of data points and adapt its model as quickly as possible while the memory and processing time is limited. In this paper, we propose a novel explicit method based on ensemble classifiers for detecting concept drift. The method processes samples one by one, and monitors the distribution of ensemble's error in order to detect probable drifts. After detection of a drift, a new classifier will be trained on the new concept in order to keep the model up-to-date. The proposed method has been evaluated on some artificial and real benchmark data sets. The experiments' results show that the proposed method is capable of detecting and adjusting to concept drifts from different types, and it has outperformed well-known state-of-the-art methods. Especially, in the case of high-speed concept drifts.