Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Interpretable Learning Algorithm Based on XGBoost for Fault Prediction in Optical Network

Not Accessible

Your library or personal account may give you access

Abstract

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.

© 2020 The Author(s)

PDF Article
More Like This
Deep Learning for Multi-Step Performance Prediction in Operational Optical Networks

Ameni Mezni, Douglas W. Charlton, Christine Tremblay, and Christian Desrosiers
STh4M.1 CLEO: Science and Innovations (CLEO:S&I) 2020

Transfer learning aided concurrent multi-alarm prediction in optical transport networks

Bing Zhang, Yongli Zhao, Yajie Li, and Jie Zhang
M4A.197 Asia Communications and Photonics Conference (ACP) 2020

Component Fault Location in Optical Networks based on Attention Mechanism with Monitoring Data

Chuidian Zeng, Jiawei Zhang, Ruikun Wang, Bojun Zhang, and Yuefeng Ji
We4B.5 European Conference and Exhibition on Optical Communication (ECOC) 2022

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved