RT Conference Proceedings
T1 On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence
A1 Lobo, Jesus L.
A1 Lana, Ibai
A1 Osaba, Eneko
A1 Del Ser, Javier
AB AI-based digital twins are at the leading edge of the Industry 4.0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis. Information collected from industrial assets is produced in a continuous fashion, yielding data streams that must be processed under stringent timing constraints. Such data streams are usually subject to non-stationary phenomena, causing that the data distribution of the streams may change, and thus the knowledge captured by models used for data analysis may become obsolete (leading to the so-called concept drift effect). The early detection of the change (drift) is crucial for updating the model's knowledge, which is challenging especially in scenarios where the ground truth associated to the stream data is not readily available. Among many other techniques, the estimation of the model's confidence has been timidly suggested in a few studies as a criterion for detecting drifts in unsupervised settings. The goal of this manuscript is to confirm and expose solidly the connection between the model's confidence in its output and the presence of a concept drift, showcasing it experimentally and advocating for a major consideration of uncertainty estimation in comparative studies to be reported in the future.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9798350339840
YR 2023
FD 2023
LK https://hdl.handle.net/11556/2645
UL https://hdl.handle.net/11556/2645
LA eng
NO Lobo , J L , Lana , I , Osaba , E & Del Ser , J 2023 , On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence . in Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023 . Proceedings - 2023 IEEE Conference on Artificial Intelligence, CAI 2023 , Institute of Electrical and Electronics Engineers Inc. , pp. 171-172 , 2023 IEEE Conference on Artificial Intelligence, CAI 2023 , Santa Clara , United States , 5/06/23 . https://doi.org/10.1109/CAI54212.2023.00081
NO conference
NO Publisher Copyright: © 2023 IEEE.
NO This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No: 101000162 (PIACERE project). This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No: 101000162 (PIACERE project).
DS TECNALIA Publications
RD 29 jul 2024