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Finding Probabilistic Rule Lists using the Minimum Description Length Principle

  • John O. R. AogaEmail author
  • Tias Guns
  • Siegfried Nijssen
  • Pierre Schaus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11198)

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.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.ICTEAM, UCLouvainOttignies-Louvain-la-NeuveBelgium
  2. 2.VUBBrusselsBelgium
  3. 3.KU LeuvenLeuvenBelgium

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