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

arXiv 2606.30322 ↗First posted 29 Jun 2026cs.LG eess.SPcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 20 references that are in the map. First posted on arXiv 29 Jun 2026.

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4 of 20 references.

References · 4

  1. 2020Learning under Concept Drift: A Reviewcited by 477
  2. 2018Online Learning: A Comprehensive Surveycited by 205
  3. 2025From Data to Decision: A Multi-Stage Framework for Class Imbalance Mitigation in Optical Network Failure Analysiscited by 1
  4. 2026Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networkscited by 1

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