Abstract
Conventional rule learning algorithms use a single heuristic for evaluating both, rule refinements and rule selection. In this paper, we argue that these two phases should be separated. Moreover, whereas rule selection proceeds in a bottom-up specific-to-general direction, rule refinement typically operates top-down. Hence, in this paper we propose that criteria for evaluating rule refinements should reflect this by operating in an inverted coverage space. We motivate this choice by examples, and show that a suitably adapted rule learning algorithm outperforms its original counter-part on a large set of benchmark problems.
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Stecher, J., Janssen, F., Fürnkranz, J. (2014). Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44845-8_8
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