On Deciding Feature Membership in Explanations of SDD & Related Classifiers

• URL: http://arxiv.org/abs/2202.07553v1
• Date: Tue, 15 Feb 2022 16:38:53 GMT
• Title: On Deciding Feature Membership in Explanations of SDD & Related Classifiers
• Authors: Xuanxiang Huang, Joao Marques-Silva
• Abstract summary: The paper shows that the feature membership problem (FMP) is hard for $SigmaP$ for a broad class of classifiers. The paper proposes propositional encodings for classifiers represented with Sentential Decision Diagrams (SDDs) and for other propositional languages.
• Score: 0.685316573653194
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: When reasoning about explanations of Machine Learning (ML) classifiers, a pertinent query is to decide whether some sensitive features can serve for explaining a given prediction. Recent work showed that the feature membership problem (FMP) is hard for $\Sigma_2^P$ for a broad class of classifiers. In contrast, this paper shows that for a number of families of classifiers, FMP is in NP. Concretely, the paper proves that any classifier for which an explanation can be computed in polynomial time, then deciding feature membership in an explanation can be decided with one NP oracle call. The paper then proposes propositional encodings for classifiers represented with Sentential Decision Diagrams (SDDs) and for other related propositional languages. The experimental results confirm the practical efficiency of the proposed approach.

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