# AutoML for Multi-Class Anomaly Compensation of Sensor Drift

arXiv: https://arxiv.org/abs/2502.19180 · HTML: https://mlsanity.com/paper/2502.19180/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Fence GAN: Towards Better Anomaly Detection](https://mlsanity.com/paper/1904.01209/) (2019)
- [Drift with Devil: Security of Multi-Sensor Fusion based Localization in High-Level Autonomous Driving under GPS Spoofing (Extended Version)](https://mlsanity.com/paper/2006.10318/) (2020)
- [On the Decision Boundary of Deep Neural Networks](https://mlsanity.com/paper/1808.05385/) (2018)
- [Drift in a Popular Metal Oxide Sensor Dataset Reveals Limitations for Gas Classification Benchmarks](https://mlsanity.com/paper/2108.08793/) (2021)
- [ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal Structural Behavior](https://mlsanity.com/paper/2407.00140/) (2024)

## Built on by (most cited first, 2 of 2)

- [S4ConvD: Adaptive Scaling and Frequency Adjustment for Energy-Efficient Sensor Networks in Smart Buildings](https://mlsanity.com/paper/2502.21035/) (2025)
- [Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance](https://mlsanity.com/paper/2506.23314/) (2025)

## Abstract (arXiv)

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.