Model tree pruning

Abstract

A model tree is a decision tree in which a specified model, such as a linear regression or naive Bayes model, is built on part of the leaf nodes. Compared with the typical decision tree in which every leaf node is assigned a class label, a model tree has several advantages: the flexibility to handle mixed attributes, a simplified tree structure, and a good potential for processing big data. This paper investigates a model tree in which the ELM model is applied to some leaf nodes of the tree and compares two fundamental strategies for generating model trees in terms of training complexity and generalization ability, namely, prepruning and postpruning. The experimental results and algorithmic analysis show that, with respect to the ELM model tree, postpruning achieves better performance than does prepruning, which has previously been universally regarded as one of the most popular decision tree generation strategies.

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Acknowledgements

We would like to express our gratitude to all those who helped me during the writing of this paper. We gratefully acknowledge the help of our supervisor, Prof. XiZhao Wang, who has offered us valuable suggestions to revise and improve this paper. This work was supported in part by the National Natural Science Foundation of China (Grant 61772344 and Grant 61732011), in part by the Natural Science Foundation of SZU (Grant 827-000140, Grant 827-000230 and Grant 2017060).

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Correspondence to Dasen Yan.

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Zhou, X., Yan, D. Model tree pruning. Int. J. Mach. Learn. & Cyber. 10, 3431–3444 (2019). https://doi.org/10.1007/s13042-019-00930-9

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Keywords

  • Model tree
  • Pruning
  • Decision tree
  • Extreme learning machine
  • ELM-Tree