  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.
