State-of-the-art results in typical classification tasks are mostly achieved
by unexplainable machine learning methods, like deep neural networks, for
instance. Contrarily, in this paper, we investigate the application of rule
learning methods in such a context. Thus, classifications become based on
comprehensible (first-order) rules, explaining the predictions made. In
general, however, rule-based classifications are less accurate than
state-of-the-art results (often significantly). As main contribution, we
introduce a voting approach combining both worlds, aiming to achieve comparable
results as (unexplainable) state-of-the-art methods, while still providing
explanations in the form of deterministic rules. Considering a variety of
benchmark data sets including a use case of significant interest to insurance
industries, we prove that our approach not only clearly outperforms ordinary
rule learning methods, but also yields results on a par with state-of-the-art
outcomes.

通过结合机器学习方法、规则学习方法和投票方法，本研究提出了一种能在提供解释性规则的同时达到与现有无法解释的机器学习方法相媲美的分类结果的方法。实验结果表明，该方法在多个基准数据集和保险业的一个关键应用案例中都明显优于普通的规则学习方法和现有的先进方法。

可解释分类的投票方法与规则学习

A Voting Approach for Explainable Classification with Rule Learning

Decision trees are popular Classification and Regression tools and, when
small-sized, easy to interpret. Traditionally, a greedy approach has been used
to build the trees, yielding a very fast training process; however, controlling
sparsity (a proxy for interpretability) is challenging. In recent studies,
optimal decision trees, where all decisions are optimized simultaneously, have
shown a better learning performance, especially when oblique cuts are
implemented. In this paper, we propose a continuous optimization approach to
build sparse optimal classification trees, based on oblique cuts, with the aim
of using fewer predictor variables in the cuts as well as along the whole tree.
Both types of sparsity, namely local and global, are modeled by means of
regularizations with polyhedral norms. The computational experience reported
supports the usefulness of our methodology. In all our data sets, local and
global sparsity can be improved without harming classification accuracy. Unlike
greedy approaches, our ability to easily trade in some of our classification
accuracy for a gain in global sparsity is shown.

本文提出了一种连续优化方法来构建精简的最佳分类树，基于斜裁剪，并旨在在整个树中使用较少的预测变量进行剪枝。该方法模拟了本地和全局稀疏性，证明了其对提高分类精度具有实用性。

最优随机分类树中的稀疏性

Sparsity in Optimal Randomized Classification Trees

PatternAttribution is a recent method, introduced in the vision domain, that
explains classifications of deep neural networks. We demonstrate that it also
generates meaningful interpretations in the language domain.

PatternAttribution 是一种最近介绍的方法，在视觉领域解释深度神经网络的分类；我们证明它也在语言领域生成有意义的解释。