Abstract:
We apply Quantile Regression (QR) for lightpath quality-of-transmission (QoT) estimation with the aim of identifying uncertain decisions and then exploit Shapley Additive...Show MoreMetadata
Abstract:
We apply Quantile Regression (QR) for lightpath quality-of-transmission (QoT) estimation with the aim of identifying uncertain decisions and then exploit Shapley Additive Explanations (SHAP) to quantify lightpath features’ importance by means of SHAP values and validate the model’s decisions in a post-processing phase. Numerical results show that our approach can eliminate more than 98% of false predictions and that SHAP values can validate up to 90% of the model's uncertain decisions.
Date of Conference: 02-06 July 2023
Date Added to IEEE Xplore: 08 August 2023
ISBN Information: