Communication Dans Un Congrès Année : 2026

Formally Correct Search for Interpretable DNFs

Résumé

Interpretable models are a key aspect of explainable machine learning. A model can be considered to be interpretable if for each decision there is an explanation involving only k features, for some small constant k. For boolean functions kappa this means that both kappa and its complement are expressible as k-DNFs. Nested k-DNFs are one such family of interpretable models. We show how to find such models and provide software, based on a formally-verified SAT encoding, to do so. We report experiments indicating that nested DNFs are an interpretable alternative to random forests while retaining the same accuracy.

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hal-05597072 , version 1 (20-04-2026)

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Imane Bousdira, Martin Cooper, Aurélie Hurault. Formally Correct Search for Interpretable DNFs. International Conference on Fundamental Approaches to Software Engineering, Apr 2026, Turin, Italy. pp.107-125, ⟨10.1007/978-3-032-22774-4_6⟩. ⟨hal-05597072⟩
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