Partially Supervised Classification for Early Concept Drift Detection

Maxime Fuccellaro, Laurent Simon, Akka Zemmari

Abstract: As more and more data is generated and stored, and as longer data streams become available, concept drift detection is becoming crucial for most real world applications. We introduce Partially Supervised Drift Detection, PSDD, a drift detection method based on Decision Trees that does not suppose any knowledge of true class labels during inference. Our approach works in any number of dimensions and is able to distinguish real from virtual drift. We successfully evaluated our method with well established datasets in the drift detection field.