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A General Framework for Induction of Decision Trees under Uncertainty

  • Chapter
Modelling with Words

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2873))

Abstract

Real data is pervaded with uncertainty. Besides, nowadays it is widely accepted the existence of other kinds of uncertainty beyond the classical probabilistic approach. As a consequence, development and adaptation of automatic knowledge acquisition techniques under uncertainty is entirely advisable. Among them the decision tree paradigm is specially suitable to comprehension and readability concerns. This paper provides the definition of a general framework for the induction of decision trees in the presence of uncertainty. A novel approach based on the concept of observational entropy is also introduced.

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Author information

Authors and Affiliations

  1. Seccióde Matemàtiques i Informàtica, ETSAB, Universitat Politècnica de Catalunya, Avda. Diagonal 649, 08028, Barcelona, Spain

    Enric Hernández

  2. Seccióde Matemàtiques i Informàtica, ETSAV, Universitat Politècnica de Catalunya, Pere Serra 1-15, 08190, Sant Cugat, Spain

    Jordi Recasens

Authors
  1. Enric Hernández
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  2. Jordi Recasens
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Editor information

Editors and Affiliations

  1. Artificial Intelligence Group, Engineering Mathematics Department, University of Bristol, BS8 1TR, UK

    Jonathan Lawry

  2. Clairvoyance Corporation, 5301 Fifth Avenue, 15232, Pittsburgh, PA, USA

    Jimi Shanahan

  3. ECECS Department, University of Cincinnati, ML 0030, 45221-0030, Cincinnati, OH, USA

    Anca L. Ralescu

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Hernández, E., Recasens, J. (2003). A General Framework for Induction of Decision Trees under Uncertainty. In: Lawry, J., Shanahan, J., L. Ralescu, A. (eds) Modelling with Words. Lecture Notes in Computer Science(), vol 2873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39906-3_2

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  • DOI: https://doi.org/10.1007/978-3-540-39906-3_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20487-9

  • Online ISBN: 978-3-540-39906-3

  • eBook Packages: Springer Book Archive

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