On Meta-Learning Rule Learning Heuristics

Abstract

The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. To that end, we let a rule learner learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance of a rule from its training set characteristics. We investigate several variations of this basic scenario, including the question whether it is better to predict the performance of the candidate rule itself or of the resulting final rule. Our experiments on a number of independent evaluation sets show that the learned heuristics outperform standard rule learning heuristics. We also analyze their behavior in coverage space.