将随机森林转为电路，实现决策解释与鲁棒性分析
Circuit Representations of Random Forests with Applications to XAI
- 将随机森林编译为电路，直接编码分类实例
- 可高效计算决策的完整原因与最短翻转路径
- 适合需要可解释性与鲁棒性分析的机器学习应用
本文提出三种贡献：首先，将随机森林分类器编译为一组电路，每个电路直接编码某类别的样本，实验表明该方法显著优于现有方案；其次，利用此电路结构，构建可处理完整通用原因的可计算电路，这些原因是生成解释的核心；最后，提出算法以计算决策的鲁棒性及所有最短翻转路径。通过实证，展示了该方法在枚举充分原因、必要原因、对比解释；计算决策鲁棒性；识别从多个数据集训练的随机森林决策最短翻转方式中的有效性。
原文摘要 · Abstract (English)
We make three contributions in this paper. First, we present an approach for compiling a random forest classifier into a set of circuits, where each circuit directly encodes the instances in some class of the classifier. We show empirically that our proposed approach is significantly more efficient than existing similar approaches. Next, we utilize this approach to further obtain circuits that are tractable for computing the complete and general reasons of a decision, which are instance abstractions that play a fundamental role in computing explanations. Finally, we propose algorithms for computing the robustness of a decision and all shortest ways to flip it. We illustrate the utility of our contributions by using them to enumerate all sufficient reasons, necessary reasons and contrastive explanations of decisions; to compute the robustness of decisions; and to identify all shortest ways to flip the decisions made by random forest classifiers learned from a wide range of datasets.
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