Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks
Ali, Yousuf Moiz, Prilepsky, Jaroslaw E., Sambo, Nicola, Pedro, João, Hosseini, Mohammad M., Napoli, Antonio, Turitsyn, Sergei K., Freire, Pedro
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Jul-30-2025
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