Details
| Original language | English |
|---|---|
| Pages (from-to) | 104-117 |
| Number of pages | 14 |
| Journal | TECHNOMETRICS |
| Volume | 66 |
| Issue number | 1 |
| Early online date | 22 Sept 2023 |
| Publication status | Published - 2024 |
Abstract
The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model’s deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called “embedding”) generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.
Keywords
- Artificial neural networks, Change point detection, Data depth, Latent feature representation, Multivariate statistical process monitoring, Online process monitoring
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
- Mathematics(all)
- Modelling and Simulation
- Mathematics(all)
- Applied Mathematics
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In: TECHNOMETRICS, Vol. 66, No. 1, 2024, p. 104-117.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Statistical Process Monitoring of Artificial Neural Networks
AU - Malinovskaya, Anna
AU - Mozharovskyi, Pavlo
AU - Otto, Philipp
N1 - Funding Information: We acknowledge the support of the cluster system team at the Leibniz University Hannover, Germany, in the production of this work. The project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 412992257.
PY - 2024
Y1 - 2024
N2 - The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model’s deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called “embedding”) generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.
AB - The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model’s deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called “embedding”) generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.
KW - Artificial neural networks
KW - Change point detection
KW - Data depth
KW - Latent feature representation
KW - Multivariate statistical process monitoring
KW - Online process monitoring
UR - http://www.scopus.com/inward/record.url?scp=85171781969&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2209.07436
DO - 10.48550/arXiv.2209.07436
M3 - Article
AN - SCOPUS:85171781969
VL - 66
SP - 104
EP - 117
JO - TECHNOMETRICS
JF - TECHNOMETRICS
SN - 0040-1706
IS - 1
ER -