Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 20 references that are in the map. First posted on arXiv 17 Jul 2025.
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References · 2
- 2019Modeling Tabular data using Conditional GANcited by 699
- 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
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