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Random Forest Pruning Techniques: A Recent Review

Author

Listed:
  • Youness Manzali

    (Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University)

  • Mohamed Elfar

    (Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University)

Abstract

Random forest is one of the most used machine learning algorithms since its high predictive performance. However, many studies criticize it for the fact that it generates a large number of trees, which requires important storage space and a significant learning time. In addition, the final model induced by RF may contain redundant trees and others that do not contribute to the prediction that may even disadvantage performance. This is why many researchers try to reduce the number of trees in a forest called forest pruning. This article presents a study of the pruning work of random forest classifiers, explains in detail the operating principle of each technique, and cites their advantages and disadvantages. Finally, it compares their classification performance in terms of accuracy, speed of learning, and complexity.

Suggested Citation

  • Youness Manzali & Mohamed Elfar, 2023. "Random Forest Pruning Techniques: A Recent Review," SN Operations Research Forum, Springer, vol. 4(2), pages 1-14, June.
    • Handle: RePEc:spr:snopef:v:4:y:2023:i:2:d:10.1007_s43069-023-00223-6
    DOI: 10.1007/s43069-023-00223-6
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    References listed on IDEAS

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    1. Zardad Khan & Asma Gul & Aris Perperoglou & Miftahuddin Miftahuddin & Osama Mahmoud & Werner Adler & Berthold Lausen, 2020. "Ensemble of optimal trees, random forest and random projection ensemble classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 97-116, March.
    2. Chung, Dongjun & Kim, Hyunjoong, 2015. "Accurate ensemble pruning with PL-bagging," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 1-13.
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