Reducing Complexity and Enhancing Predictive Power of ML-Based Lightpath QoT Estimation via SHAP-Assisted Feature Selection-Reference-Cited by-同舟云学术

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Reducing Complexity and Enhancing Predictive Power of ML-Based Lightpath QoT Estimation via SHAP-Assisted Feature Selection

Published:2024-05-06 Issue: Volume:13.4 Page:1-6
ISSN:
Container-title:2024 International Conference on Optical Network Design and Modeling (ONDM)
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Author:

Fawaz Hussein¹,Arpanaei Farhad²,Andreoletti Davide³,Sbeity Ihab¹,Hernández José Alberto²,Larrabeiti David²,Ayoub Omran³

Affiliation:

1. Lebanese University,Faculty of Science,Lebanon

2. Universidad Carlos III,Department of Telematic Engineering,de Madrid,Spain

3. University of Applied Sciences of Southern Switzerland,Department of Innovative Technologies,Switzerland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10582547/10582564/10582777.pdf?arnumber=10582777

Reference24 articles.

1. Machine learning techniques for quality of transmission estimation in optical networks;Yvan;Journal of Optical Communications and Networking,2021

2. Machine-Learning Method for Quality of Transmission Prediction of Unestablished Lightpaths

3. Accurate Quality of Transmission Estimation With Machine Learning

4. Deep Quantile Regression for QoT Inference and Confident Decision Making

5. Learning quantile QoT models to address uncertainty over unseen lightpaths

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