Enhancing Concept Drift Detection with Simulated Recurrence
This paper focuses on the concept drift detection and proposes how to extend the functionality of a statistical concept drift detector for unlabeled observations. For those algorithms the previously developed approach so-called simulated recurrence is implemented as a separate module. It provides information regarding the possible data distribution after concept drift detection. The proposed approaches were compared with five detection algorithms on the basis of computer experiments which were carried ut on the UCI benchmark datasets.
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