Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks
Yousuf Moiz Ali, Jaroslaw E. Prilepsky, Nicola Sambo, João Pedro, Mohammad M. Hosseini, Antonio Napoli, Sergei K. Turitsyn, Pedro Freire
arXiv:2507.21119v1h-index: 25
Originality Synthesis-oriented
AI Analysis
This work tackles failure detection in optical networks, an incremental comparison of existing imbalance mitigation methods.
The paper compared pre-, in-, and post-processing techniques to address class imbalance in optical network failure detection, finding that Threshold Adjustment provided the highest F1 gain of 15.3%, while Random Under-sampling offered the fastest inference speed.
We compare pre-, in-, and post-processing techniques for class imbalance mitigation in optical network failure detection. Threshold Adjustment achieves the highest F1 gain (15.3%), while Random Under-sampling (RUS) offers the fastest inference, highlighting a key performance-complexity trade-off.