CD2A: Concept Drift Detection Approach Toward Imbalanced Data Stream

In recent years, data stream has been considered as one of the primary sources of big data. Data stream has grown very rapidly in the last decades. Data stream environment has many features distinguishing the batch learning data which arrives on the fly with high speed. Data stream mining has attracted research focus due to its presence in many real-time applications such as telecommunication, networking, and banking. One of the most important challenges in data stream is the distribution of data is changing continuously which is leading to the phenomenon called “concept drift.” Another issue for streaming data is dealing with imbalanced class in the dataset. Many classification algorithms have been made to cope with the concept drift; however, many of them are dealing with the drift from the balanced data. In this paper, we propose a model called “CD2A: Concept Drift Detection Approach Toward Imbalanced Data Stream” which aims to handle the imbalanced data and detect the concept drift and behave equally with different types of drift. The algorithm was evaluated on real and synthetic dataset and compared with leading edge methods AWE, SMOTE, SERA, and OOB. Our method performs significantly better average prediction accuracy than the other compared methods.