On the reliable detection of concept drift from streaming unlabeled data

@article{Sethi2017OnTR,
  title={On the reliable detection of concept drift from streaming unlabeled data},
  author={Tegjyot Singh Sethi and Mehmed M. Kantardzic},
  journal={Expert Syst. Appl.},
  year={2017},
  volume={82},
  pages={77-99}
}
Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and be able to adapt to them, over time. Detecting drifts has traditionally been approached as a supervised task, with labeled data constantly being used for… CONTINUE READING
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