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The Continuous—Function Attribute Class in Decision Tree Induction

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Discovey Science (DS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1532))

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Abstract

The automatic extraction of knowledge from data gathered from a dynamic system is an important task, because continuous measurement acquisition provides an increasing amount of numerical data. On an abstract layer these data can generally be modeled as continuous functions over time. In this article we present an approach to handle continuous-function attributes efficiently in decision tree induction, if the entropy minimalization heuristics is applied

It is shown how time series based upon continuous functions could be preprocessed if used in decision tree induction. A proof is given, that a piecewise linear approximation of the individual time series or the underlying continuous functions could improve the efficiency of the induction task

Research supported by FNK-Forschungsförderung University of Bremen.

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Author information

Authors and Affiliations

  1. Center for Computing Technology, University of Bremen, FB3, P.O.Box 33 04 40, D-28334, Bremen

    Michael Boronowsky

Authors
  1. Michael Boronowsky
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Editor information

Editors and Affiliations

  1. Department of Informatics, Kyushu University, Fukuoka, 812-8581, USA

    Setsuo Arikawa

  2. Institute of Scientific and Industrial Research Devision of Intelligent Systems Science, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan

    Hiroshi Motoda

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© 1998 Springer-Verlag Berlin Heidelberg

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Boronowsky, M. (1998). The Continuous—Function Attribute Class in Decision Tree Induction. In: Arikawa, S., Motoda, H. (eds) Discovey Science. DS 1998. Lecture Notes in Computer Science(), vol 1532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49292-5_24

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  • DOI: https://doi.org/10.1007/3-540-49292-5_24

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65390-5

  • Online ISBN: 978-3-540-49292-4

  • eBook Packages: Springer Book Archive

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