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DOI: 10.4230/OASIcs.DX.2026.6

Geometric Feature Selection for Interpretable SVM Classifiers in Cyber-Physical Systems

Authors: Leonie Hatte, Pauline Ribot, and Elodie Chanthery

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

Abstract

Identifying mode-transition conditions in Cyber-Physical Systems (CPS) is fundamental to model-based diagnosis, but learning them from scarce, multivariate time-series data requires boundary expressions that remain interpretable in physical variables. A central challenge is that some features are actively detrimental: their inclusion distorts the orientation of the learned boundary, degrading robustness to future crossings without affecting training accuracy. We introduce SVM-GRFE (Geometric Recursive Feature Elimination for SVM), a feature selection method that operates on polynomial monomials rather than raw variables, jointly assessing accuracy and geometric criteria that capture each monomial’s structural role in boundary orientation; the boundary is back-projected analytically to an explicit polynomial inequality in physical variables. We prove SVM-GRFE returns an interpretable, geometrically stable, and robust boundary. On seven synthetic scenarios, SVM-GRFE achieves 33%-85% monomial reduction, matches or outperforms SVM-RFE in five out of seven scenarios and consistently outperforms SVM-RFE under noise at equal complexity, most notably under heteroscedastic noise.

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Leonie Hatte, Pauline Ribot, and Elodie Chanthery. Geometric Feature Selection for Interpretable SVM Classifiers in Cyber-Physical Systems. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 6:1-6:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{hatte_et_al:OASIcs.DX.2026.6,
  author =	{Hatte, Leonie and Ribot, Pauline and Chanthery, Elodie},
  title =	{{Geometric Feature Selection for Interpretable SVM Classifiers in Cyber-Physical Systems}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{6:1--6:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.6},
  URN =		{urn:nbn:de:0030-drops-278200},
  doi =		{10.4230/OASIcs.DX.2026.6},
  annote =	{Keywords: Feature Selection, SVM, Geometric boundary learning, CPS}
}

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Geometric Feature Selection for Interpretable SVM Classifiers in Cyber-Physical Systems

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