# Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks

arXiv: https://arxiv.org/abs/2602.10401 · HTML: https://mlsanity.com/paper/2602.10401/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [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)

## Built on by (most cited first, 1 of 1)

- [Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection](https://mlsanity.com/paper/2606.30322/) (2026)

## Abstract (arXiv)

We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventional static models while maintaining low latency.