布尔分类器的计算可懂性
On the Computational Intelligibility of Boolean Classifiers
摘要：
在本文中，我们调查的布尔分类器的计算可理解性，其特征在于他们的能力，回答XAI查询在多项式时间。正在考虑的分类器是决策树，DNF公式，决策列表，决策规则，树集成，和布尔神经网络。使用9个XAI查询，包括解释查询和验证查询，我们发现分类器家族之间存在很大的可理解性差距。一方面，所有9个XAI查询对于决策树都是易处理的。另一方面，没有一个是易于处理的DNF公式，决策列表，随机森林，提升决策树，布尔多层感知器，和二进制神经网络。
英文摘要：
In this paper, we investigate the computational intelligibility of Boolean classifiers, characterized by their ability to answer XAI queries in polynomial time. The classifiers under consideration are decision trees, DNF formulae, decision lists, decision rules, tree ensembles, and Boolean neural nets. Using 9 XAI queries, including both explanation queries and verification queries, we show the existence of large intelligibility gap between the families of classifiers. On the one hand, all the 9 XAI queries are tractable for decision trees. On the other hand, none of them is tractable for DNF formulae, decision lists, random forests, boosted decision trees, Boolean multilayer perceptrons, and binarized neural networks.
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。
