Meta-Learning Rule Learning Heuristics

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Meta-Learning Rule Learning Heuristics
The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to 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 from 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.
Frederik Janssen, Johannes Fürnkranz
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where LWA
Authors Frederik Janssen, Johannes Fürnkranz
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