  Concept drift detection has attracted considerable attention due to its
importance in many real-world applications such as health monitoring and fault
diagnosis. Conventionally, most advanced approaches will be of poor performance
when the evaluation criteria of the environment has changed (i.e. concept
drift), either can only detect and adapt to virtual drift. In this paper, we
propose a new approach to detect real-drift in the chunk data stream with
limited annotations based on concept confusion. When a new data chunk arrives,
we use both real labels and pseudo labels to update the model after prediction
and drift detection. In this context, the model will be confused and yields
prediction difference once drift occurs. We then adopt cosine similarity to
measure the difference. And an adaptive threshold method is proposed to find
the abnormal value. Experiments show that our method has a low false alarm rate
and false negative rate with the utilization of different classifiers.
