2026-09-28T11:51:29Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27820 2026-09-28T06:35:12Z ddc:004 open_access

Geometric Feature Selection for Interpretable SVM Classifiers in Cyber-Physical Systems Hatte, Leonie Ribot, Pauline Chanthery, Elodie Feature Selection SVM Geometric boundary learning CPS 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Leonie Hatte and Pauline Ribot and Elodie Chanthery 2026 Is Part Of OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.DX.2026.6 urn:nbn:de:0030-drops-278200 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.6 eng https://creativecommons.org/licenses/by/4.0/legalcode