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International Conference on Machine Learning and Cybernetics

ICMLC 2014: Machine Learning and Cybernetics pp 207–218Cite as

Study and Improvement of Ordinal Decision Trees Based on Rank Entropy

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 481))

Abstract

Decision tree is one of the most commonly used methods of machine learning, and ordinal decision tree is an important way to deal with ordinal classification problems. Through researches and analyses on ordinal decision trees based on rank entropy, the rank mutual information for every cut of each continuous-valued attribute is necessary to determine during the selection of expanded attributes for constructing decision trees based on rank entropy in ordinal classification. Then we need to compare these values of rank mutual information to get the maximum which corresponds to the expanded attribute. As the computational complexity is high, an improved algorithm which establishes a mathematical model is proposed. The improved algorithm is theoretically proved that it only traverses the unstable cut-points without computing the values of stable cut-points. Therefore, the computational efficiency of constructing decision trees is greatly improved. Experiments also confirm that the computational time of the improved algorithm can be reduced greatly.

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Author information

Authors and Affiliations

  1. Machine Learning Center, Faculty of Mathematics and Computer Science, Hebei University, Baoding, 071002, China

    Jiankai Chen, Junhai Zhai & Xizhao Wang

Authors
  1. Jiankai Chen
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  2. Junhai Zhai
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  3. Xizhao Wang
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Corresponding author

Correspondence to Junhai Zhai .

Editor information

Editors and Affiliations

  1. Hebei University, Baoding, China

    Xizhao Wang

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. South China University of Technology, Guangzhou, China

    Patrick Chan

  4. Hebei University, Baoding, China

    Qiang He

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© 2014 Springer-Verlag Berlin Heidelberg

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Chen, J., Zhai, J., Wang, X. (2014). Study and Improvement of Ordinal Decision Trees Based on Rank Entropy. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_22

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  • DOI: https://doi.org/10.1007/978-3-662-45652-1_22

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45651-4

  • Online ISBN: 978-3-662-45652-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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