  Classifying streaming data requires the development of methods which are
computationally efficient and able to cope with changes in the underlying
distribution of the stream, a phenomenon known in the literature as concept
drift. We propose a new method for detecting concept drift which uses an
Exponentially Weighted Moving Average (EWMA) chart to monitor the
misclassification rate of an streaming classifier. Our approach is modular and
can hence be run in parallel with any underlying classifier to provide an
additional layer of concept drift detection. Moreover our method is
computationally efficient with overhead O(1) and works in a fully online manner
with no need to store data points in memory. Unlike many existing approaches to
concept drift detection, our method allows the rate of false positive
detections to be controlled and kept constant over time.
