Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams

Lukasz Korycki, Bartosz Krawczyk

Abstract: Highlights•Enhancing drift adaptation in sparsely labeled data streams at no additional cost.•Instance exploitation techniques to empower active learning and avoid underfitting.•Ensemble architectures adaptively switching between risky and standard adaptation.•Flexible framework that can be used to enhance any online active learning algorithm.•In-depth analysis of enhanced drift adaptation via extensive experimental study.