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Random Fuzzy Granular Decision Tree

Author

Listed:
  • Wei Li
  • Xiaoyu Ma
  • Yumin Chen
  • Bin Dai
  • Runjing Chen
  • Chao Tang
  • Youmeng Luo
  • Kaiqiang Zhang

Abstract

In this study, the classification problem is solved from the view of granular computing. That is, the classification problem is equivalently transformed into the fuzzy granular space to solve. Most classification algorithms are only adopted to handle numerical data; random fuzzy granular decision tree (RFGDT) can handle not only numerical data but also nonnumerical data like information granules. Measures can be taken in four ways as follows. First, an adaptive global random clustering (AGRC) algorithm is proposed, which can adaptively find the optimal cluster centers and maximize the ratio of interclass standard deviation to intraclass standard deviation, and avoid falling into local optimal solution; second, on the basis of AGRC, a parallel model is designed for fuzzy granulation of data to construct granular space, which can greatly enhance the efficiency compared with serial granulation of data; third, in the fuzzy granular space, we design RFGDT to classify the fuzzy granules, which can select important features as tree nodes based on information gain ratio and avoid the problem of overfitting based on the pruning algorithm proposed. Finally, we employ the dataset from UC Irvine Machine Learning Repository for verification. Theory and experimental results prove that RFGDT has high efficiency and accuracy and is robust in solving classification problems.

Suggested Citation

  • Wei Li & Xiaoyu Ma & Yumin Chen & Bin Dai & Runjing Chen & Chao Tang & Youmeng Luo & Kaiqiang Zhang, 2021. "Random Fuzzy Granular Decision Tree," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-17, June.
  • Handle: RePEc:hin:jnlmpe:5578682
    DOI: 10.1155/2021/5578682
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    Cited by:

    1. Qiangqiang Chen & Linjie He & Yanan Diao & Kunbin Zhang & Guoru Zhao & Yumin Chen, 2022. "A Novel Neighborhood Granular Meanshift Clustering Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-15, December.

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