  The notion of concept drift refers to the phenomenon that the distribution,
which is underlying the observed data, changes over time. We are interested in
an identification of those features, that are most relevant for the observed
drift. We distinguish between drift inducing features, for which the observed
feature drift cannot be explained by any other feature, and faithfully drifting
features, which correlate with the present drift of other features. This notion
gives rise to minimal subsets of the feature space, which are able to
characterize the observed drift as a whole. We relate this problem to the
problems of feature selection and feature relevance learning, which allows us
to derive a detection algorithm. We demonstrate its usefulness on different
benchmarks.
