Automl for Multi-Class Anomaly Compensation of Sensor Drift

authored by: 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.
Organisation(s): Machine Learning Section
Institute of Artificial Intelligence
L3S Research Centre
Institute of Information Processing
Automatic Image Interpretation Section
External Organisation(s): University of Southern California
Type: Article
Journal: Measurement: Journal of the International Measurement Confederation
Volume: 250
ISSN: 0263-2241
Publication date: 15.06.2025
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Instrumentation, Electrical and Electronic Engineering
Electronic version(s): https://doi.org/10.1016/j.measurement.2025.117097 (Access: Open)

BibTeX

@article{f9cb1e0998f94d16a02675339243d22d,
title = "Automl for Multi-Class Anomaly Compensation of Sensor Drift",
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.",
keywords = "Automated machine learning, Sensor measurements, Sensordrift",
author = "Schaller, {Melanie Christine} and Mathis Kruse and Antonio Ortega and Marius Lindauer and Bodo Rosenhahn",
note = "Publisher Copyright: {\textcopyright} 2025 The Authors",
year = "2025",
month = jun,
day = "15",
doi = "10.1016/j.measurement.2025.117097",
language = "English",
volume = "250",
journal = "Measurement: Journal of the International Measurement Confederation",
issn = "0263-2241",
publisher = "Elsevier BV",
}

Publication Details

Automl for Multi-Class Anomaly Compensation of Sensor Drift

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