  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.
