提出可解释的嵌套k-DNF模型，兼顾准确率与人类可读性。
Interpretable DNFs
- 用嵌套结构构建k-DNF，保证正负决策都有短解释。
- 实验显示其准确率接近深度树，解释更简洁。
- 适合需要透明决策过程的高风险场景。
一个分类器若其每个决策都能提供小规模、易理解的解释，则被视为可解释。在布尔域上，DNF公式可作为二分类器κ。对κ的正类决策，解释长度受其项大小限制——只需给出一个真值项即可。为使正负决策均具备可解释性，要求κ及其补集¬κ均可表示为项大小有界的DNF。本文研究互补项大小也受限于k的k-DNF家族，对比了深度-k决策树与新型嵌套k-DNF模型。实验表明，嵌套k-DNF在可解释性与准确率之间取得良好平衡，是决策树的有力替代方案。
原文摘要 · Abstract (English)
A classifier is considered interpretable if each of its decisions has an explanation which is small enough to be easily understood by a human user. A DNF formula can be seen as a binary classifier $κ$ over boolean domains. The size of an explanation of a positive decision taken by a DNF $κ$ is bounded by the size of the terms in $κ$, since we can explain a positive decision by giving a term of $κ$ that evaluates to true. Since both positive and negative decisions must be explained, we consider that interpretable DNFs are those $κ$ for which both $κ$ and $\overlineκ$ can be expressed as DNFs composed of terms of bounded size. In this paper, we study the family of $k$-DNFs whose complements can also be expressed as $k$-DNFs. We compare two such families, namely depth-$k$ decision trees and nested $k$-DNFs, a novel family of models. Experiments indicate that nested $k$-DNFs are an interesting alternative to decision trees in terms of interpretability and accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。