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Generalized Conditional Entropy and a Metric Splitting Criterion for Decision Trees

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Advances in Knowledge Discovery and Data Mining (PAKDD 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3918))

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Abstract

We examine a new approach to building decision tree by introducing a geometric splitting criterion, based on the properties of a family of metrics on the space of partitions of a finite set. This criterion can be adapted to the characteristics of the data sets and the needs of the users and yields decision trees that have smaller sizes and fewer leaves than the trees built with standard methods and have comparable or better accuracy.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, University of Massachusetts at Boston, Boston, Massachusetts, 02125, USA

    Dan A. Simovici

  2. Faculty of Computer and Information Systems, Technical University of Szeczin, Poland

    Szymon Jaroszewicz

Authors
  1. Dan A. Simovici
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  2. Szymon Jaroszewicz
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Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore

    Wee-Keong Ng

  2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8505, Tokyo, Japan

    Masaru Kitsuregawa

  3. School of Computer Science and Technology, Heilongjiang University, China

    Jianzhong Li

  4. School of Computer Engineering, Nanyang Technological University, 639798, Singapore, Singapore

    Kuiyu Chang

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© 2006 Springer-Verlag Berlin Heidelberg

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Simovici, D.A., Jaroszewicz, S. (2006). Generalized Conditional Entropy and a Metric Splitting Criterion for Decision Trees. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_7

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  • DOI: https://doi.org/10.1007/11731139_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33206-0

  • Online ISBN: 978-3-540-33207-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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