Automl for Multi-Class Anomaly Compensation of Sensor Drift

verfasst von

Melanie Christine Schaller, Mathis Kruse, Antonio Ortega, Marius Lindauer, Bodo Rosenhahn

Abstract

Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as it progressively degrades the performance of machine learning models over time. Our findings indicate that the standard cross-validation method used in existing model training overestimates performance by inadequately accounting for drift. This is primarily because typical cross-validation techniques allow data instances to appear in both training and testing sets, thereby distorting the accuracy of the predictive evaluation. As a result, these models are unable to precisely predict future drift effects, compromising their ability to generalize and adapt to evolving data conditions. This paper presents two solutions: (1) a novel sensor drift compensation learning paradigm for validating models, and (2) automated machine learning (AutoML) techniques to enhance classification performance and compensate sensor drift. By employing strategies such as data balancing, meta-learning, automated ensemble learning, hyperparameter optimization, feature selection, and boosting, our AutoML-DC (Drift Compensation) model significantly improves classification performance against sensor drift. AutoML-DC further adapts effectively to varying drift severities.

Organisationseinheit(en)

Fachgebiet Maschinelles Lernen
Institut für Künstliche Intelligenz
Forschungszentrum L3S
Institut für Informationsverarbeitung
Fachgebiet Automatische Bildinterpretation

Externe Organisation(en)

University of Southern California

Typ

Artikel

Journal

Measurement: Journal of the International Measurement Confederation

Band

250

ISSN

0263-2241

Publikationsdatum

02.2025

Publikationsstatus

Angenommen/Im Druck

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Instrumentierung, Elektrotechnik und Elektronik

Elektronische Version(en)

https://doi.org/10.1016/j.measurement.2025.117097 (Zugang: Offen)