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Beam-Influenced Attribute Selector for Producing Stable Reduct

Author

Listed:
  • Wangwang Yan

    (School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China)

  • Jing Ba

    (School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China)

  • Taihua Xu

    (School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China)

  • Hualong Yu

    (School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China)

  • Jinlong Shi

    (School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China)

  • Bin Han

    (School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China)

Abstract

Attribute reduction is a critical topic in the field of rough set theory. Currently, to further enhance the stability of the derived reduct, various attribute selectors are designed based on the framework of ensemble selectors. Nevertheless, it must be pointed out that some limitations are concealed in these selectors: (1) rely heavily on the distribution of samples; (2) rely heavily on the optimal attribute. To generate the reduct with higher stability, a novel beam-influenced selector (BIS) is designed based on the strategies of random partition and beam. The scientific novelty of our selector can be divided into two aspects: (1) randomly partition samples without considering the distribution of samples; (2) beam-based selections of features can save the selector from the dependency of the optimal attribute. Comprehensive experiments using 16 UCI data sets show the following: (1) the stability of the derived reducts may be significantly enhanced by using our selector; (2) the reducts generated based on the proposed selector can provide competent performance in classification tasks.

Suggested Citation

  • Wangwang Yan & Jing Ba & Taihua Xu & Hualong Yu & Jinlong Shi & Bin Han, 2022. "Beam-Influenced Attribute Selector for Producing Stable Reduct," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:553-:d:746746
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    References listed on IDEAS

    as
    1. Dawen Yan & Guotai Chi & Kin Keung Lai, 2020. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models," Mathematics, MDPI, vol. 8(8), pages 1-27, August.
    2. Yan Chen & Jingjing Song & Keyu Liu & Yaojin Lin & Xibei Yang, 2020. "Combined Accelerator for Attribute Reduction: A Sample Perspective," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, February.
    3. Agustin Pérez-Martín & Agustin Pérez-Torregrosa & Alejandro Rabasa & Marta Vaca, 2020. "Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    4. Mohamed Abd Elaziz & Laith Abualigah & Dalia Yousri & Diego Oliva & Mohammed A. A. Al-Qaness & Mohammad H. Nadimi-Shahraki & Ahmed A. Ewees & Songfeng Lu & Rehab Ali Ibrahim, 2021. "Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection," Mathematics, MDPI, vol. 9(21), pages 1-17, November.
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