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Attribute Reduction Based on Consistent Covering Rough Set and Its Application

Author

Listed:
  • Jianchuan Bai
  • Kewen Xia
  • Yongliang Lin
  • Panpan Wu

Abstract

As an important processing step for rough set theory, attribute reduction aims at eliminating data redundancy and drawing useful information. Covering rough set, as a generalization of classical rough set theory, has attracted wide attention on both theory and application. By using the covering rough set, the process of continuous attribute discretization can be avoided. Firstly, this paper focuses on consistent covering rough set and reviews some basic concepts in consistent covering rough set theory. Then, we establish the model of attribute reduction and elaborate the steps of attribute reduction based on consistent covering rough set. Finally, we apply the studied method to actual lagging data. It can be proved that our method is feasible and the reduction results are recognized by Least Squares Support Vector Machine (LS-SVM) and Relevance Vector Machine (RVM). Furthermore, the recognition results are consistent with the actual test results of a gas well, which verifies the effectiveness and efficiency of the presented method.

Suggested Citation

  • Jianchuan Bai & Kewen Xia & Yongliang Lin & Panpan Wu, 2017. "Attribute Reduction Based on Consistent Covering Rough Set and Its Application," Complexity, Hindawi, vol. 2017, pages 1-9, October.
    • Handle: RePEc:hin:complx:8986917
    DOI: 10.1155/2017/8986917
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    Cited by:

    1. Wenbiao Yang & Kewen Xia & Tiejun Li & Min Xie & Fei Song, 2021. "A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM," Mathematics, MDPI, vol. 9(3), pages 1-34, February.
    2. Yuan Gao & Xiangjian Chen & Xibei Yang & Pingxin Wang & Jusheng Mi, 2019. "Ensemble-Based Neighborhood Attribute Reduction: A Multigranularity View," Complexity, Hindawi, vol. 2019, pages 1-17, November.

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