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An efficient random forests algorithm for high dimensional data classification

Author

Listed:
  • Qiang Wang

    (Shenzhen University)

  • Thanh-Tung Nguyen

    (Thuyloi University
    Unité de Modélisation Mathématiques et Informatique des Systèmes Complexes)

  • Joshua Z. Huang

    (Shenzhen University)

  • Thuy Thi Nguyen

    (Vietnam National University of Agriculture)

Abstract

In this paper, we propose a new random forest (RF) algorithm to deal with high dimensional data for classification using subspace feature sampling method and feature value searching. The new subspace sampling method maintains the diversity and randomness of the forest and enables one to generate trees with a lower prediction error. A greedy technique is used to handle cardinal categorical features for efficient node splitting when building decision trees in the forest. This allows trees to handle very high cardinality meanwhile reducing computational time in building the RF model. Extensive experiments on high dimensional real data sets including standard machine learning data sets and image data sets have been conducted. The results demonstrated that the proposed approach for learning RFs significantly reduced prediction errors and outperformed most existing RFs when dealing with high-dimensional data.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:advdac:v:12:y:2018:i:4:d:10.1007_s11634-018-0318-1
    DOI: 10.1007/s11634-018-0318-1
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Erika Slabber & Tanja Verster & Riaan de Jongh, 2023. "Some Insights about the Applicability of Logistic Factorisation Machines in Banking," Risks, MDPI, vol. 11(3), pages 1-21, February.
    2. Donghoun Lee, 2024. "Driving Safety Area Classification for Automated Vehicles Based on Data Augmentation Using Generative Models," Sustainability, MDPI, vol. 16(11), pages 1-19, May.
    3. Oyebayo Ridwan Olaniran & Ali Rashash R. Alzahrani, 2023. "On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression," Mathematics, MDPI, vol. 11(24), pages 1-29, December.

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