Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection
Machine Learning
2026-06-29 v1 Signal Processing
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.
Cite
@article{arxiv.2606.30322,
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},
journal= {arXiv preprint arXiv:2606.30322},
year = {2026}
}
Comments
Accepted for oral presentation at the European Conference on Optical Communication (ECOC 2026)