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Making Decision Trees More Accurate by Losing Information

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Foundations of Information and Knowledge Systems (FoIKS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1762))

Abstract

In this investigation we discuss how to improve the quality of decision trees, one of the classification techniques in compensation for small loss of amount of information. To do that, we assume a semantic hierarchy among classes which is ignored in conventional stories. The basic idea comes from relaxing class membership by using the hierarchy and we explore how to preserve the precision of classification in a sense of entropy.

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Author information

Authors and Affiliations

  1. Dept. of Elect. and Elect., Engineering, Hosei University, Kajinocho 3-7-2, Koganei, Tokyo, Japan

    Takao Miura

  2. SANNO College, Kamikasuya 1563, Isehara, Kanagawa, Japan

    Isamu Shioya & Mikihiro Mori

Authors
  1. Takao Miura
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  2. Isamu Shioya
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  3. Mikihiro Mori
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Editor information

Editors and Affiliations

  1. Institut für Informatik, Technische Universität Clausthal, Julius-Albert-Str. 4, 38678, Clausthal-Zellerfeld, Germany

    Klaus-Dieter Schewe

  2. Institut für Informatik, Technische Universität Cottbus, Postfach 101344, 03013, Cottbus, Germany

    Bernhard Thalheim

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© 2000 Springer-Verlag Berlin Heidelberg

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Miura, T., Shioya, I., Mori, M. (2000). Making Decision Trees More Accurate by Losing Information. In: Schewe, KD., Thalheim, B. (eds) Foundations of Information and Knowledge Systems. FoIKS 2000. Lecture Notes in Computer Science, vol 1762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46564-2_13

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  • DOI: https://doi.org/10.1007/3-540-46564-2_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67100-8

  • Online ISBN: 978-3-540-46564-5

  • eBook Packages: Springer Book Archive

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