  We introduce Class Distribution Monitoring (CDM), an effective concept-drift
detection scheme that monitors the class-conditional distributions of a
datastream. In particular, our solution leverages multiple instances of an
online and nonparametric change-detection algorithm based on QuantTree. CDM
reports a concept drift after detecting a distribution change in any class,
thus identifying which classes are affected by the concept drift. This can be
precious information for diagnostics and adaptation. Our experiments on
synthetic and real-world datastreams show that when the concept drift affects a
few classes, CDM outperforms algorithms monitoring the overall data
distribution, while achieving similar detection delays when the drift affects
all the classes. Moreover, CDM outperforms comparable approaches that monitor
the classification error, particularly when the change is not very apparent.
Finally, we demonstrate that CDM inherits the properties of the underlying
change detector, yielding an effective control over the expected time before a
false alarm, or Average Run Length (ARL$_0$).
