  Recent research yielded a wide array of drift detectors. However, in order to
achieve remarkable performance, the true class labels must be available during
the drift detection phase. This paper targets at detecting drift when the
ground truth is unknown during the detection phase. To that end, we introduce
Gaussian Split Detector (GSD) a novel drift detector that works in batch mode.
GSD is designed to work when the data follow a normal distribution and makes
use of Gaussian mixture models to monitor changes in the decision boundary. The
algorithm is designed to handle multi-dimension data streams and to work
without the ground truth labels during the inference phase making it pertinent
for real world use. In an extensive experimental study on real and synthetic
datasets, we evaluate our detector against the state of the art. We show that
our detector outperforms the state of the art in detecting real drift and in
ignoring virtual drift which is key to avoid false alarms.
