An Empirical Investigation of the Trade-Off between Consistency and Coverage in Rule Learning Heuristics

Abstract

In this paper, we argue that search heuristics for inductive rule learning algorithms typically trade off consistency and coverage, and we investigate this trade-off by determining optimal parameter settings for five different parametrized heuristics. This empirical comparison yields several interesting results. Of considerable practical importance are the default values that we establish for these heuristics, and for which we show that they outperform commonly used instantiations of these heuristics. We also gain some theoretical insights. For example, we note that it is important to relate the rule coverage to the class distribution, but that the true positive rate should be weighted more heavily than the false positive rate. We also find that the optimal parameter settings of these heuristics effectively implement quite similar preference criteria.