Analysis and Mitigation of Unwanted Biases in ML-based QoT Classification Tasks
Paper in proceeding, 2024
We address the problem of mitigating biases in models used for the quality of transmission prediction. The proposed method reduces the relative accuracy difference between samples with different feature values by up to 45%.
Machine learning
Quality of transmission
Classification
Optical networks
Author
Carlos Natalino Da Silva
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Behnam Shariati
Fraunhofer-Institut fur Nachrichtentechnik Heinrich-Hertz-Institut - HHI
Pooyan Safari
Fraunhofer-Institut fur Nachrichtentechnik Heinrich-Hertz-Institut - HHI
Johannes Fischer
Fraunhofer-Institut fur Nachrichtentechnik Heinrich-Hertz-Institut - HHI
Paolo Monti
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Conference on Optical Fiber Communication, Technical Digest Series
M1H.3
9781957171326 (ISBN)
Optical Fiber Communications Conference and Exhibition (OFC)
San Diego, CA, USA,
San Diego, CA, USA,
Providing Resilient & secure networks [Operating on Trusted Equipment] to CriTical infrastructures (PROTECT)
VINNOVA (2020-03506), 2021-02-01 -- 2024-01-31.
Areas of Advance
Information and Communication Technology
Subject Categories
Telecommunications
Communication Systems
Signal Processing
DOI
10.1364/OFC.2024.M1H.3
ISBN
9781957171326