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

arXiv: https://arxiv.org/abs/2606.30322 · HTML: https://mlsanity.com/paper/2606.30322/

## 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.

## Builds on (most cited first, 4 of 4)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [From Data to Decision: A Multi-Stage Framework for Class Imbalance Mitigation in Optical Network Failure Analysis](https://mlsanity.com/paper/2509.00057/) (2025)
- [Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks](https://mlsanity.com/paper/2602.10401/) (2026)

## 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.