Abstract

This paper introduces a framework of Automated Machine Learning (AutoML) for automating soft-failure classification in optical networks with minimal human intervention. The selected model achieves high classification metrics and is over 3 times faster in training, yielding a significantly better trade-off between classification metrics and computational cost. These results demonstrate that AutoML can deliver efficient, high-quality models, offering a scalable and low-effort solution for failure management in optical networks.