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Ensemble of optimal trees, random forest and random projection ensemble classification

Author

Listed:
  • Zardad Khan

    (Abdul Wali Khan University
    University of Essex)

  • Asma Gul

    (University of Essex
    Shaheed Benazir Bhutto Women University)

  • Aris Perperoglou

    (University of Essex)

  • Miftahuddin Miftahuddin

    (University of Essex
    Syiah Kuala University)

  • Osama Mahmoud

    (University of Essex
    Helwan University
    University of Bristol)

  • Werner Adler

    (University of Erlangen-Nuremberg)

  • Berthold Lausen

    (University of Essex)

Abstract

The predictive performance of a random forest ensemble is highly associated with the strength of individual trees and their diversity. Ensemble of a small number of accurate and diverse trees, if prediction accuracy is not compromised, will also reduce computational burden. We investigate the idea of integrating trees that are accurate and diverse. For this purpose, we utilize out-of-bag observations as a validation sample from the training bootstrap samples, to choose the best trees based on their individual performance and then assess these trees for diversity using the Brier score on an independent validation sample. Starting from the first best tree, a tree is selected for the final ensemble if its addition to the forest reduces error of the trees that have already been added. Our approach does not use an implicit dimension reduction for each tree as random project ensemble classification. A total of 35 bench mark problems on classification and regression are used to assess the performance of the proposed method and compare it with random forest, random projection ensemble, node harvest, support vector machine, kNN and classification and regression tree. We compute unexplained variances or classification error rates for all the methods on the corresponding data sets. Our experiments reveal that the size of the ensemble is reduced significantly and better results are obtained in most of the cases. Results of a simulation study are also given where four tree style scenarios are considered to generate data sets with several structures.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:advdac:v:14:y:2020:i:1:d:10.1007_s11634-019-00364-9
    DOI: 10.1007/s11634-019-00364-9
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    References listed on IDEAS

    as
    1. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    2. Panagiotis Tzirakis & Christos Tjortjis, 2017. "T3C: improving a decision tree classification algorithm’s interval splits on continuous attributes," 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. 11(2), pages 353-370, June.
    3. Timothy I. Cannings & Richard J. Samworth, 2017. "Random-projection ensemble classification," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 959-1035, September.
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    Cited by:

    1. Tiffany Elsten & Mark Rooij, 2022. "SUBiNN: a stacked uni- and bivariate kNN sparse ensemble," 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. 16(4), pages 847-874, December.
    2. Youness Manzali & Mohamed Elfar, 2023. "Random Forest Pruning Techniques: A Recent Review," SN Operations Research Forum, Springer, vol. 4(2), pages 1-14, June.
    3. Gerhard Tutz, 2022. "Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 241-263, July.
    4. Muhammed-Fatih Kaya, 2022. "Pattern Labelling of Business Communication Data," Group Decision and Negotiation, Springer, vol. 31(6), pages 1203-1234, December.

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