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SPAARC: A Fast Decision Tree Algorithm

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Data Mining (AusDM 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 996))

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Abstract

Decision trees are a popular method of data-mining and knowledge discovery, capable of extracting hidden information from datasets consisting of both nominal and numerical attributes. However, their need to test the suitability of every attribute at every tree node, in addition to testing every possible split-point for every numerical attribute can be expensive computationally, particularly for datasets with high dimensionality. This paper proposes a method for speeding up the decision tree induction process called SPAARC, consisting of two components to address these issues – sampling of the numeric attribute tree-node split-points and dynamically adjusting the node attribute selection space. Further, these methods can be applied to almost any decision tree algorithm. To confirm its validity, SPAARC has been tested and compared against an implementation of the CART algorithm using 18 freely-available datasets from the UCI data repository. Results from this testing indicate the two components of SPAARC combined have minimal effect on decision tree classification accuracy yet reduce model build times by as much as 69%.

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Acknowledgements

This research is supported by an Australian Government Research Training Program (RTP) scholarship.

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Authors and Affiliations

  1. Charles Sturt University, Panorama Avenue, Bathurst, NSW, 2795, Australia

    Darren Yates & Md Zahidul Islam

  2. University of Sydney Business School, The University of Sydney, Sydney, NSW, 2006, Australia

    Junbin Gao

Authors
  1. Darren Yates
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  2. Md Zahidul Islam
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  3. Junbin Gao
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Corresponding author

Correspondence to Darren Yates .

Editor information

Editors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  2. University of Auckland, Auckland, New Zealand

    Yun Sing Koh

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. Data Science and Engineering, Australian Taxation Office, Canberra, Australia

    Graco Warwick

  5. Department of Information Technology, University of Wollongong, Wollongong, NSW, Australia

    David Stirling

  6. School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia

    Chang-Tsun Li

  7. School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia

    Zahidul Islam

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Yates, D., Islam, M.Z., Gao, J. (2019). SPAARC: A Fast Decision Tree Algorithm. In: Islam, R., et al. Data Mining. AusDM 2018. Communications in Computer and Information Science, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-6661-1_4

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  • DOI: https://doi.org/10.1007/978-981-13-6661-1_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6660-4

  • Online ISBN: 978-981-13-6661-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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