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Oracle Coached Decision Trees and Lists

  • Ulf Johansson
  • Cecilia Sönströd
  • Tuve Löfström
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6065)

Abstract

This paper introduces a novel method for obtaining increased predictive performance from transparent models in situations where production input vectors are available when building the model. First, labeled training data is used to build a powerful opaque model, called an oracle. Second, the oracle is applied to production instances, generating predicted target values, which are used as labels. Finally, these newly labeled instances are utilized, in different combinations with normal training data, when inducing a transparent model. Experimental results, on 26 UCI data sets, show that the use of oracle coaches significantly improves predictive performance, compared to standard model induction. Most importantly, both accuracy and AUC results are robust over all combinations of opaque and transparent models evaluated. This study thus implies that the straightforward procedure of using a coaching oracle, which can be used with arbitrary classifiers, yields significantly better predictive performance at a low computational cost.

Keywords

Decision trees Rule learning Coaching 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ulf Johansson
    • 1
  • Cecilia Sönströd
    • 1
  • Tuve Löfström
    • 1
  1. 1.CSL@BS Research Group School of Business and InformaticsUniversity of BoråsSweden

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