A Voting Approach for Explainable Classification with Rule Learning

TL;DR

This paper introduces a voting method that combines rule-based explainability with the high accuracy of state-of-the-art classifiers, demonstrating comparable performance on benchmark datasets.

Contribution

It proposes a novel voting approach that enhances rule learning methods to achieve accuracy similar to unexplainable models while maintaining interpretability.

Findings

Outperforms traditional rule learning methods

Achieves results comparable to state-of-the-art classifiers

Effective on diverse benchmark datasets

Abstract

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.