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Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5632))

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Abstract

A number of two-class classification methods first discretize each attribute of two given training sets and then construct a propositional DNF formula that evaluates to True for one of the two discretized training sets and to False for the other one. The formula is not just a classification tool but constitutes a useful explanation for the differences between the two underlying populations if it can be comprehended by humans and is reliable. This paper shows that comprehensibility as well as reliability of the formulas can sometimes be improved using a discretization scheme where linear combinations of a small number of attributes are discretized.

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Authors and Affiliations

  1. Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083, U.S.A.

    Klaus Truemper

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  1. Klaus Truemper
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Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Körnerstr. 10, 04107, Leipzig, Deutschland, Germany

    Petra Perner

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Truemper, K. (2009). Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_1

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  • DOI: https://doi.org/10.1007/978-3-642-03070-3_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03069-7

  • Online ISBN: 978-3-642-03070-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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