In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherungs-Aktiengesellschaft which is an insurance company offering diverse services in Germany.

翻译：在本文中,我们提出了一种模块化方法,将(随机)机器学习中最先进的方法与传统规则学习方法结合起来,以便为大型数据集的分类提供高效和可扩展的算法,同时仍然可以解释。除了评估我们对通用大型数据集MNIST、时装-MNIST和IMDB的方法外,我们还介绍了关于牙科帐单可解释分类的新结果。后一案例研究来自与Allianz Privater Krankenversicherungs-Aktiengesellschaft的工业合作,后者是一家在德国提供多种服务的保险公司。