To Tune or not to Tune: Rule Evaluation for Metaheuristic-based Sequential Covering Algorithms

While many papers propose innovative methods for constructing individual rules in separate-and-conquer rule learning algorithms, comparatively few study the heuristic rule evaluation functions used in these algorithms to ensure that the selected rules combine into a good rule set. Underestimating the impact of this component has led to suboptimal design choices in many algorithms. The main goal of this paper is to demonstrate the importance of heuristic rule evaluation functions by improving existing rule induction techniques and to provide guidelines for algorithm designers.We first select optimal heuristic rule learning functions for several metaheuristic-based algorithms and empirically compare the resulting heuristics across algorithms. This results in large and significant improvements of the predictive accuracy for two techniques. We find that despite the absence of a global optimal choice for all algorithms, good default choices seem to exist for families of algorithms. A near-optimal selection can thus be found for new algorithms with minor experimental tuning. A major contribution is made towards balancing a model’s predictive accuracy with its comprehensibility, as the parametrized heuristics offer an unmatched flexibility when it comes to setting the trade-off between accuracy and comprehensibility.