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Detecting drifts in data is essential for machine learning applications, aschanges in the statistics of processed data typically has a profound influenceon the performance of trained models. Most of the available drift detectionmethods are either supervised and require access to the true labels duringinference time, or they are completely unsupervised and aim for changes indistributions without taking label information into account. We propose a noveltask-sensitive semi-supervised drift detection scheme, which utilizes labelinformation while training the initial model, but takes into account thatsupervised label information is no longer available when using the model duringinference. It utilizes a constrained low-dimensional embedding representationof the input data. This way, it is best suited for the classification task. Itis able to detect real drift, where the drift affects the classificationperformance, while it properly ignores virtual drift, where the classificationperformance is not affected by the drift. In the proposed framework, the actualmethod to detect a change in the statistics of incoming data samples can bechosen freely. Experimental evaluation on nine benchmarks datasets, withdifferent types of drift, demonstrates that the proposed framework can reliablydetect drifts, and outperforms state-of-the-art unsupervised drift detectionapproaches.

Task-Sensitive Concept Drift Detector with Constraint Embedding