A Remark on Concept Drift for Dependent Data
Fabian Hinder Valerie Vaquet Barbara Hammer
Concept drift, i.e., the change of the data generating distribution, can
render machine learning models inaccurate. Several works address the phenomenon
of concept drift in the streaming context usually assuming that consecutive
data points are independent of each other. To generalize to dependent data,
many authors link the notion of concept drift to time series. In this work, we
show that the temporal dependencies are strongly influencing the sampling
process. Thus, the used definitions need major modifications. In particular, we
show that the notion of stationarity is not suited for this setup and discuss
alternatives. We demonstrate that these alternative formal notions describe the
observable learning behavior in numerical experiments.