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ComEnVprs: A Novel Approach for Inducing Decision Tree Classifiers

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Advanced Data Mining and Applications (ADMA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4093))

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Abstract

This paper presents a new approach for inducing decision trees by combining information entropy criteria with VPRS based methods. From the angle of rough set theory, when inducing decision trees, entropy based methods emphasize the effect of class distribution. Whereas the rough set based approaches emphasize the effect of certainty. The presented approach takes the advantages of both criteria for inducing decision trees. Comparisons between the presented approach and the fundamental information entropy based method on some data sets from the UCI Machine Learning Repository are also reported.

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Author information

Authors and Affiliations

  1. School of Mathematics & Statistics, Northeast Normal University, Jilin, 130024, China

    Shuqin Wang

  2. Institute of Computational Intelligence, Northeast Normal University, Jilin, 130024, China

    Jinmao Wei & Junping You

  3. Open Symbol Computation and Knowledge Engineering Laboratory of State Education, Jilin University, Jilin, 130024, China

    Jinmao Wei & Dayou Liu

Authors
  1. Shuqin Wang
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  2. Jinmao Wei
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  3. Junping You
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  4. Dayou Liu
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electronic Engineering, The University of Queensland, Queensland, Australia

    Xue Li

  2. University of Alberta, Canada

    Osmar R. Zaïane

  3. Northwest Polytechnical University, China

    Zhanhuai Li

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, S., Wei, J., You, J., Liu, D. (2006). ComEnVprs: A Novel Approach for Inducing Decision Tree Classifiers. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_13

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  • DOI: https://doi.org/10.1007/11811305_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37025-3

  • Online ISBN: 978-3-540-37026-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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