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Learning from high dimensional data based on weighted feature importance in decision tree ensembles

Author

Listed:
  • Nayiri Galestian Pour

    (University of Tehran)

  • Soudabeh Shemehsavar

    (University of Tehran)

Abstract

Learning from high dimensional data has been utilized in various applications such as computational biology, image classification, and finance. Most classical machine learning algorithms fail to give accurate predictions in high dimensional settings due to the enormous feature space. In this article, we present a novel ensemble of classification trees based on weighted random subspaces that aims to adjust the distribution of selection probabilities. In the proposed algorithm base classifiers are built on random feature subspaces in which the probability that influential features will be selected for the next subspace, is updated by incorporating grouping information based on previous classifiers through a weighting function. As an interpretation tool, we show that variable importance measures computed by the new method can identify influential features efficiently. We provide theoretical reasoning for the different elements of the proposed method, and we evaluate the usefulness of the new method based on simulation studies and real data analysis.

Suggested Citation

  • Nayiri Galestian Pour & Soudabeh Shemehsavar, 2024. "Learning from high dimensional data based on weighted feature importance in decision tree ensembles," Computational Statistics, Springer, vol. 39(1), pages 313-342, February.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:1:d:10.1007_s00180-023-01347-3
    DOI: 10.1007/s00180-023-01347-3
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    References listed on IDEAS

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    1. Blaser, Rico & Fryzlewicz, Piotr, 2016. "Random rotation ensembles," LSE Research Online Documents on Economics 62182, London School of Economics and Political Science, LSE Library.
    2. Baoxun Xu & Joshua Zhexue Huang & Graham Williams & Qiang Wang & Yunming Ye, 2012. "Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 8(2), pages 44-63, April.
    3. Ahn, Hongshik & Moon, Hojin & Fazzari, Melissa J. & Lim, Noha & Chen, James J. & Kodell, Ralph L., 2007. "Classification by ensembles from random partitions of high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6166-6179, August.
    4. Ruoqing Zhu & Donglin Zeng & Michael R. Kosorok, 2015. "Reinforcement Learning Trees," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1770-1784, December.
    5. 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.
    6. Zhao, He & Williams, Graham J. & Huang, Joshua Zhexue, 2017. "wsrf: An R Package for Classification with Scalable Weighted Subspace Random Forests," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i03).
    7. Antonio R. Linero, 2018. "Bayesian Regression Trees for High-Dimensional Prediction and Variable Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 626-636, April.
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