IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v39y2024i1d10.1007_s00180-023-01347-3.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-023-01347-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-023-01347-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Blaser, Rico & Fryzlewicz, Piotr, 2016. "Random rotation ensembles," LSE Research Online Documents on Economics 62182, London School of Economics and Political Science, LSE Library.
    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. 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.
    7. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Liu & Veronika Ročková & Yuexi Wang, 2021. "Variable selection with ABC Bayesian forests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 453-481, July.
    2. Jingxuan Luo & Xuejiao Li & Chongxiu Yu & Gaorong Li, 2023. "Multiclass Sparse Discriminant Analysis Incorporating Graphical Structure Among Predictors," Journal of Classification, Springer;The Classification Society, vol. 40(3), pages 614-637, November.
    3. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    4. Ke-Lin Du & Rengong Zhang & Bingchun Jiang & Jie Zeng & Jiabin Lu, 2025. "Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus," Mathematics, MDPI, vol. 13(4), pages 1-49, February.
    5. Bergsma, Wicher P, 2020. "Regression with I-priors," Econometrics and Statistics, Elsevier, vol. 14(C), pages 89-111.
    6. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    7. Crystal T. Nguyen & Daniel J. Luckett & Anna R. Kahkoska & Grace E. Shearrer & Donna Spruijt‐Metz & Jaimie N. Davis & Michael R. Kosorok, 2020. "Estimating individualized treatment regimes from crossover designs," Biometrics, The International Biometric Society, vol. 76(3), pages 778-788, September.
    8. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    9. Yatracos, Yannis G., 2018. "Residual'S Influence Index (Rinfin), Bad Leverage And Unmasking In High Dimensional L2-Regression," IRTG 1792 Discussion Papers 2018-060, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Silke Janitza & Ender Celik & Anne-Laure Boulesteix, 2018. "A computationally fast variable importance test for random forests for high-dimensional data," 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. 12(4), pages 885-915, December.
    11. Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2023. "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Journal of Econometrics, Elsevier, vol. 232(1), pages 52-69.
    12. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
    13. Rodney A. Sparapani & Brent R. Logan & Martin J. Maiers & Purushottam W. Laud & Robert E. McCulloch, 2023. "Nonparametric failure time: Time‐to‐event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures," Biometrics, The International Biometric Society, vol. 79(4), pages 3023-3037, December.
    14. Niu, Zibo & Wang, Chenlu & Zhang, Hongwei, 2023. "Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models," International Review of Financial Analysis, Elsevier, vol. 89(C).
    15. Qiang Wang & Thanh-Tung Nguyen & Joshua Z. Huang & Thuy Thi Nguyen, 2018. "An efficient random forests algorithm for high dimensional data 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. 12(4), pages 953-972, December.
    16. Dimitris Mylonas & Serge Caparos & Jules Davidoff, 2022. "Augmenting a colour lexicon," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
    17. Alberto Caron & Gianluca Baio & Ioanna Manolopoulou, 2022. "Estimating individual treatment effects using non‐parametric regression models: A review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1115-1149, July.
    18. Fuli Zhang & Kung‐Sik Chan, 2023. "Random projection ensemble classification with high‐dimensional time series," Biometrics, The International Biometric Society, vol. 79(2), pages 964-974, June.
    19. Leoni, Leonardo & De Carlo, Filippo & Abaei, Mohammad Mahdi & BahooToroody, Ahmad & Tucci, Mario, 2023. "Failure diagnosis of a compressor subjected to surge events: A data-driven framework," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    20. Pedro Delicado & Daniel Peña, 2023. "Understanding complex predictive models with ghost variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 107-145, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:39:y:2024:i:1:d:10.1007_s00180-023-01347-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.