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.

翻译：典型分类任务中的最新成果大多由不可解释的机器学习方法（如深度神经网络）实现。相反，本文研究了规则学习方法在此类场景中的应用，使得分类基于可理解的（一阶）规则，从而解释预测结果。然而，基于规则的分类通常显著低于最新方法的准确率。作为主要贡献，我们引入了一种结合两者优点的投票方法，旨在达到与（不可解释的）最新方法相当的结果，同时仍以确定性规则的形式提供解释。通过在多个基准数据集（包括一个保险行业高度关注的用例）上的验证，我们证明该方法不仅显著优于普通规则学习方法，而且能获得与最新成果相媲美的结果。