Adaptive Ensemble Active Learning for Drifting Data Stream Mining

@inproceedings{Krawczyk2019AdaptiveEA,
  title={Adaptive Ensemble Active Learning for Drifting Data Stream Mining},
  author={B. Krawczyk and Alberto Cano},
  booktitle={IJCAI},
  year={2019}
}
Learning from data streams is among the most vital contemporary fields in machine learning and data mining. Streams pose new challenges to learning systems, due to their volume and velocity, as well as ever-changing nature caused by concept drift. Vast majority of works for data streams assume a fully supervised learning scenario, having an unrestricted access to class labels. This assumption does not hold in real-world applications, where obtaining ground truth is costly and time-consuming… Expand
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