A Voting Approach for Explainable Classification with Rule Learning
This addresses the trade-off between accuracy and explainability in machine learning, particularly for domains like insurance, though it is incremental as it builds on existing rule learning and voting techniques.
The paper tackles the problem of achieving high accuracy in classification tasks while maintaining explainability, by introducing a voting approach that combines rule learning with state-of-the-art methods, resulting in performance on par with unexplainable methods and clear improvements over ordinary rule learning.
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.