Abstract
An important task in data mining is that of rule discovery in supervised data. Well-known examples include rule-based classification and subgroup discovery. Motivated by the need to succinctly describe an entire labeled dataset, rather than accurately classify the label, we propose an MDL-based supervised rule discovery task. The task concerns the discovery of a small rule list where each rule captures the probability of the Boolean target attribute being true. Our approach is built on a novel combination of two main building blocks: (i) the use of the Minimum Description Length (MDL) principle to characterize good-and-small sets of probabilistic rules, (ii) the use of branch-and-bound with a best-first search strategy to find better-than-greedy and optimal solutions for the proposed task. We experimentally show the effectiveness of our approach, by providing a comparison with other supervised rule learning algorithms on real-life datasets.
J.O.R. Aoga—This author is supported by the FRIA-FNRS (Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture, Belgium).
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Notes
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All logarithms are to base 2 and by convention, we use \(0\log 0 = 0\).
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Note that by convention the size of the default rule is \(m_2=0\).
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Aoga, J.O.R., Guns, T., Nijssen, S., Schaus, P. (2018). Finding Probabilistic Rule Lists using the Minimum Description Length Principle. In: Soldatova, L., Vanschoren, J., Papadopoulos, G., Ceci, M. (eds) Discovery Science. DS 2018. Lecture Notes in Computer Science(), vol 11198. Springer, Cham. https://doi.org/10.1007/978-3-030-01771-2_5
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