Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection

Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro, Sasipim Srivallapanondh, Antonio Napoli +2 more
6/29/2026
cs.LGeess.SP

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Abstract

We propose a hybrid active-online learning framework for label-efficient concept drift adaptation in optical network failure detection. Using margin-based selective labeling, our method achieves nearceiling accuracy and AUC scores while querying only 3.4% of streaming samples, with negligible latency overhead compared to static inference.

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Cite this paper

@article{ali2026hybrid,
  title  = {Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection},
  author = {Yousuf Moiz Ali and Jaroslaw E. Prilepsky and João Pedro and Sasipim Srivallapanondh and Antonio Napoli and Sergei K. Turitsyn and Pedro Freire},
  year   = {2026},
  eprint = {2606.30322},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2606.30322v1}
}

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