Mitigating Concept Drift via Rejection

@inproceedings{Gpfert2018MitigatingCD,
  title={Mitigating Concept Drift via Rejection},
  author={Jan Philip G{\"o}pfert and Barbara Hammer and Heiko Wersing},
  booktitle={ICANN},
  year={2018}
}
Learning in non-stationary environments is challenging, because under such conditions the common assumption of independent and identically distributed data does not hold; when concept drift is present it necessitates continuous system updates. In recent years, several powerful approaches have been proposed. However, these models typically classify any input, regardless of their confidence in the classification – a strategy, which is not optimal, particularly in safety-critical environments… CONTINUE READING
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