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Rule Extraction with Guaranteed Fidelity

  • Ulf Johansson
  • Rikard König
  • Henrik Linusson
  • Tuve Löfström
  • Henrik Boström
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 437)

Abstract

This paper extends the conformal prediction framework to rule extraction, making it possible to extract interpretable models from opaque models in a setting where either the infidelity or the error rate is bounded by a predefined significance level. Experimental results on 27 publicly available data sets show that all three setups evaluated produced valid and rather efficient conformal predictors. The implication is that augmenting rule extraction with conformal prediction allows extraction of models where test set errors or test sets infidelities are guaranteed to be lower than a chosen acceptable level. Clearly this is beneficial for both typical rule extraction scenarios, i.e., either when the purpose is to explain an existing opaque model, or when it is to build a predictive model that must be interpretable.

Keywords

Rule extraction Conformal prediction Decision trees 

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Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Ulf Johansson
    • 1
  • Rikard König
    • 1
  • Henrik Linusson
    • 1
  • Tuve Löfström
    • 1
  • Henrik Boström
    • 2
  1. 1.School of Business and ITUniversity of BoråsSweden
  2. 2.Department of Systems and Computer SciencesStockholm UniversitySweden

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