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A new pruning method for decision tree based on structural risk of leaf node

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Abstract

Pruning is an effective technique in improving the generalization performance of decision tree. However, most of the existing methods are time-consuming or unsuitable for small dataset. In this paper, a new pruning algorithm based on structural risk of leaf node is proposed. The structural risk is measured by the product of the accuracy and the volume (PAV) in leaf node. The comparison experiments with Cost-Complexity Pruning using cross-validation (CCP-CV) algorithm on some benchmark datasets show that PAV pruning largely reduces the time cost of CCP-CV, while the test accuracy of PAV pruning is close to that of CCP-CV.

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Acknowledgments

This work was partially supported by the Natural Science Foundations of Fujian Province of China under Grant No.2011J01373 and the Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P.R. China under Grant No. SCIP2011004.

Conflict of interest

None declared.

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Authors and Affiliations

  1. Department of Automation, Xiamen University, Xiamen, 361005, People’s Republic of China

    Linkai Luo, Xiaodong Zhang, Hong Peng, Weihang Lv & Yan Zhang

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  1. Linkai Luo
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  2. Xiaodong Zhang
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  3. Hong Peng
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  4. Weihang Lv
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  5. Yan Zhang
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Correspondence to Linkai Luo.

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Luo, L., Zhang, X., Peng, H. et al. A new pruning method for decision tree based on structural risk of leaf node. Neural Comput & Applic 22 (Suppl 1), 17–26 (2013). https://doi.org/10.1007/s00521-012-1055-6

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  • DOI: https://doi.org/10.1007/s00521-012-1055-6

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