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