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International Conference on Machine Learning and Cybernetics

ICMLC 2014: Machine Learning and Cybernetics pp 241–251Cite as

Parallel Ordinal Decision Tree Algorithm and Its Implementation in Framework of MapReduce

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

Abstract

Ordinal decision tree (ODT) can effectively deal with monotonic classification problems. However, it is difficult for the existing ordinal decision tree algorithms to learning ODT from large data sets. In order to deal with the problem of generating an ODT from large datasets, this paper presents a parallel processing mechanism in the framework of MapReduce. Similar to the general ordinal decision tree algorithms, the rank mutual information (RMI) is still used to select the extended attributes. Differing from the calculation of RMI in the previous algorithms, this paper applies a strategy of attribute parallelization to calculate the RMI. Experiments on large ordered data sets (which are generated artificially) confirm that our proposed algorithm is feasible. Experimental results show that our algorithm is effective and efficient from three aspects: speed-up, scale-up and size-up.

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Author information

Authors and Affiliations

  1. College of Mathematics and Computer Science, Hebei University, Baoding, 071002, China

    Shanshan Wang, Junhai Zhai, Hong Zhu & Xizhao Wang

Authors
  1. Shanshan Wang
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  2. Junhai Zhai
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  3. Hong Zhu
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  4. 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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Wang, S., Zhai, J., Zhu, H., Wang, X. (2014). Parallel Ordinal Decision Tree Algorithm and Its Implementation in Framework of MapReduce. 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_25

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  • DOI: https://doi.org/10.1007/978-3-662-45652-1_25

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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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