  Data distributions in streaming environments are usually not stationary. In
order to maintain a high predictive quality at all times, online learning
models need to adapt to distributional changes, which are known as concept
drift. The timely and robust identification of concept drift can be difficult,
as we never have access to the true distribution of streaming data. In this
work, we propose a novel framework for the detection of real concept drift,
called ERICS. By treating the parameters of a predictive model as random
variables, we show that concept drift corresponds to a change in the
distribution of optimal parameters. To this end, we adopt common measures from
information theory. The proposed framework is completely model-agnostic. By
choosing an appropriate base model, ERICS is also capable to detect concept
drift at the input level, which is a significant advantage over existing
approaches. An evaluation on several synthetic and real-world data sets
suggests that the proposed framework identifies concept drift more effectively
and precisely than various existing works.
