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 · 2507.21119

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

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Abstract

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.

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Yousuf Moiz Ali, Jaroslaw E. Prilepsky, Nicola Sambo, João Pedro, Mohammad M. Hosseini, Antonio Napoli, Sergei K. Turitsyn, Pedro Freire. 2025-07-17. Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks. https://arxiv.org/abs/2507.21119

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