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This research article presents a cause-aware failure detection scheme for optical transport networks using the interpretable XGBoost algorithm. The study highlights the limitations of traditional neural networks in failure detection due to their black-box nature and proposes XGBoost for its ability to provide feature importance rankings and consistent feature attribution through SHAP values. Experimental results demonstrate that the proposed scheme achieves an F1 score greater than 98% for detecting failure

Author

cannotcry21

Language

EN