关于SDD及相关分类器的分类中特征隶属度的判定
On Deciding Feature Membership in Explanations of SDD & Related Classifiers
摘要：
在对机器学习（ML）分类器的解释进行推理时，相关的查询是决定某些敏感特征是否可以用于解释给定的预测。最近的工作表明，特征隶属度问题（FMP）是很难的$Σ_2^P$的广泛类别的分类。相反，本文表明，对于一些家庭的分类，FMP是在NP。具体地说，本文证明了任何分类器，其解释可以在多项式时间内计算，然后决定特征成员的解释可以决定一个NP预言机调用。然后，本文提出了命题编码的分类表示与句子决策图（SDDs）和其他相关的命题语言。实验结果证实了所提出的方法的实际效率。
英文摘要：
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 $Σ_2^P$ 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
