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.

该研究提出了一种新型批处理漂移检测器（GSD），能在没有真实类别标签的情况下监测数据流中的漂移变化，并且在实验中对比了其他算法，证明了该检测器在检测真实漂移和忽略虚假漂移方面的出色性能。