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：在典型分类任务中，最先进的结果大多由深度神经网络等不可解释的机器学习方法实现。与此相反，本文研究了规则学习方法在此类情境中的应用——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先进方法的准确率。作为主要贡献，我们提出了一种融合两种方法的投票机制，旨在在获得与（不可解释的）最先进方法可比结果的同时，仍能以确定性规则形式提供解释。通过涵盖保险行业重大应用场景的多种基准数据集验证，我们证明该方法不仅显著优于普通规则学习方法，而且达到了与最先进成果相当的性能。