  Concept drift detection is a crucial task in data stream evolving
environments. Most of state of the art approaches designed to tackle this
problem monitor the loss of predictive models. However, this approach falls
short in many real-world scenarios, where the true labels are not readily
available to compute the loss. In this context, there is increasing attention
to approaches that perform concept drift detection in an unsupervised manner,
i.e., without access to the true labels. We propose a novel approach to
unsupervised concept drift detection based on a student-teacher learning
paradigm. Essentially, we create an auxiliary model (student) to mimic the
behaviour of the primary model (teacher). At run-time, our approach is to use
the teacher for predicting new instances and monitoring the mimicking loss of
the student for concept drift detection. In a set of experiments using 19 data
streams, we show that the proposed approach can detect concept drift and
present a competitive behaviour relative to the state of the art approaches.
