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The effects of pruning methods on the predictive accuracy of induced decision trees

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  • Floriana Esposito
  • Donato Malerba
  • Giovanni Semeraro
  • Valentina Tamma

Abstract

Several methods have been proposed in the literature for decision tree (post)‐pruning. This article presents a unifying framework according to which any pruning method can be defined as a four‐tuple (Space, Operators, Evaluation function, Search strategy), and the pruning process can be cast as an optimization problem. Six well‐known pruning methods are investigated by means of this framework and their common aspects, strengths and weaknesses are described. Furthermore, a new empirical analysis of the effect of post‐pruning on both the predictive accuracy and the size od induced decision trees is reported. The experimental comparison of the pruning methods involves 14 datasets and is based on the cross‐validation procedure. The results confirm most of the conclusions drawn in a previous comparison based on the holdout procedure. Copyright © 1999 John Wiley & Sons, Ltd.

Suggested Citation

  • Floriana Esposito & Donato Malerba & Giovanni Semeraro & Valentina Tamma, 1999. "The effects of pruning methods on the predictive accuracy of induced decision trees," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 15(4), pages 277-299, October.
    • Handle: RePEc:wly:apsmbi:v:15:y:1999:i:4:p:277-299
    DOI: 10.1002/(SICI)1526-4025(199910/12)15:43.0.CO;2-B
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    1. Hang Ha & Chinh Luu & Quynh Duy Bui & Duy-Hoa Pham & Tung Hoang & Viet-Phuong Nguyen & Minh Tuan Vu & Binh Thai Pham, 2021. "Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 1247-1270, October.

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