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 +3 more
7/17/2025
cs.LGeess.SPphysics.optics

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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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Cite this paper

@article{ali2025pre,
  title  = {Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks},
  author = {Yousuf Moiz Ali and Jaroslaw E. Prilepsky and Nicola Sambo and João Pedro and Mohammad M. Hosseini and Antonio Napoli and Sergei K. Turitsyn and Pedro Freire},
  year   = {2025},
  eprint = {2507.21119},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.21119v1}
}

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