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arXiv · 2606.30322

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·Sergei K. Turitsyn·Pedro Freire
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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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Yousuf Moiz Ali, Jaroslaw E. Prilepsky, João Pedro, Sasipim Srivallapanondh, Antonio Napoli, Sergei K. Turitsyn, Pedro Freire. 2026-06-29. Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection. https://arxiv.org/abs/2606.30322

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