Interpretability analysis of Symbolic Regression models for dynamical systems
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- @InProceedings{Calapristi:2024:ICCAD,
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author = "Marco Calapristi and Luca Patane and
Francesca Sapuppo and Maria Gabriella Xibilia",
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title = "Interpretability analysis of Symbolic Regression
models for dynamical systems",
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booktitle = "2024 International Conference on Control, Automation
and Diagnosis (ICCAD)",
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year = "2024",
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month = may,
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keywords = "genetic algorithms, genetic programming, Measurement,
Visualization, Accuracy, System dynamics, Machine
learning, Predictive models",
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ISSN = "2767-9896",
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DOI = "
doi:10.1109/ICCAD60883.2024.10553801",
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abstract = "Symbolic Regression (SR) is a machine learning
paradigm that focuses on the automatic discovery of
mathematical equations that best describe the
relationship between input and output features in
experimental datasets. Such an approach leads to
interpretable models that make it easy to incorporate
the knowledge available in the system. This article
addresses the application of SR as an interpretable
modelling approach for industrial applications
involving nonlinear dynamical systems. Interpretability
in model identification is crucial for understanding
system processes. While SR models are inherently
interpretable, the inclusion of explainability
techniques such as SHAP is being explored to improve
model validation while facilitating model
interpretation by process technologists. In this paper,
the Narendra-Li system, which is commonly used as a
testbed for dynamical system identification, is
considered. The performance of the proposed solutions
is evaluated using appropriate interpretability metrics
that provide insight into their efficiency in capturing
the underlying dynamics of the system.",
-
notes = "Also known as \cite{10553801}",
- }
Genetic Programming entries for
Marco Calapristi
Luca Patane
Francesca Sapuppo
Maria Gabriella Xibilia
Citations