提出新算法，高效生成多分类贝叶斯网络的可解释逻辑公式。
Scaling the Explanation of Multi-Class Bayesian Network Classifiers
- 将多分类贝叶斯网络编译为逻辑公式，支持非二元分类。
- 编译速度显著提升，输出结果为可分解的否定正则形式电路。
- 适合需要逻辑推理解释模型决策的研究者使用。
我们提出一种新算法，用于将贝叶斯网络分类器（BNC）编译为类公式。类公式是表示分类器输入-输出行为的逻辑公式，在近期利用逻辑推理解释分类器决策的研究中至关重要。与先前针对BNC类公式的编译工作相比，本算法不局限于二元分类，编译时间显著提升，并输出以否定正则形式（NNF）表示的电路，具备或分解性，这对计算分类器解释具有重要意义。
原文摘要 · Abstract (English)
We propose a new algorithm for compiling Bayesian network classifier (BNC) into class formulas. Class formulas are logical formulas that represent a classifier's input-output behavior, and are crucial in the recent line of work that uses logical reasoning to explain the decisions made by classifiers. Compared to prior work on compiling class formulas of BNCs, our proposed algorithm is not restricted to binary classifiers, shows significant improvement in compilation time, and outputs class formulas as negation normal form (NNF) circuits that are OR-decomposable, which is an important property when computing explanations of classifiers.
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