Interpretable Learning Algorithm Based on XGBoost for Fault Prediction in Optical Network
Chunyu Zhang, Danshi Wang, Chuang Chen Song, Lingling Wang, Jianan Song, Luyao Guan, Min Zhang · 2020
We propose a fault prediction scheme using interpretable XGBoost based on actual datasets, which not only achieves high accuracy (99.72%) and low positive rate (0.18%), but also reveals the five most remarkable features that caused the fault.