An Efficient Concept Drift Detection Method for Streaming Data under Limited Labeling

In data stream analysis, detecting the concept drift accu- rately is important to maintain the classification performance. Most drift detection methods assume that the class labels become available immedi- ately after a data sample arrives. However, it is unrealistic to attempt to acquire all of the labels when processing the data streams, as labeling costs are high and much time is needed. In this paper, we propose a concept drift detection method under the assumption that there is limited access or no access to class labels. The proposed method detects concept drift on unla- beled data streams based on the class label information which is predicted by a classifier or a virtual classifier. Experimental results on synthetic and real streaming data show that the proposed method is competent to detect the concept drift on unlabeled data stream. key words: conce