@inproceedings {pub4741,
	title = {Task-Sensitive Concept Drift Detector with Constraint Embedding},
	author = {Andrea Castellani AND Sebastian Schmitt AND Barbara Hammer},
	year = {2021},
	abstract = {Detecting drifts in data is essential for machine
learning applications, as changes in the statistics of processed
data typically has a profound influence on the performance of
trained models. Most of the available drift detection methods are
either supervised and require access to the true labels during
inference time, or they are completely unsupervised and aim for
changes in distributions without taking label information into
account. We propose a novel task-sensitive semi-supervised drift
detection scheme, which utilizes label information while training
the initial model, but takes into account that supervised label
information is no longer available when using the model during
inference. It utilizes a constrained low-dimensional embedding
representation of the input data. This way, it is best suited for
the classification task. It is able to detect real drift, where the drift
affects the classification performance, while it properly ignores
virtual drift, where the classification performance is not affected
by the drift. In the proposed framework, the actual method to
detect a change in the statistics of incoming data samples can
be chosen freely. Experimental evaluation on nine benchmarks
datasets, with different types of drift, demonstrates that the
proposed framework can reliably detect drifts, and outperforms
state-of-the-art unsupervised drift detection approaches.},
	publisher = {IEEE},
	booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}
}