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Parallel Selector for Feature Reduction

Author

Listed:
  • Zhenyu Yin

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

  • Yan Fan

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

  • Pingxin Wang

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

  • Jianjun Chen

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

Abstract

In the field of rough set, feature reduction is a hot topic. Up to now, to better guide the explorations of this topic, various devices regarding feature reduction have been developed. Nevertheless, some challenges regarding these devices should not be ignored: (1) the viewpoint provided by a fixed measure is underabundant; (2) the final reduct based on single constraint is sometimes powerless to data perturbation; (3) the efficiency in deriving the final reduct is inferior. In this study, to improve the effectiveness and efficiency of feature reduction algorithms, a novel framework named parallel selector for feature reduction is reported. Firstly, the granularity of raw features is quantitatively characterized. Secondly, based on these granularity values, the raw features are sorted. Thirdly, the reordered features are evaluated again. Finally, following these two evaluations, the reordered features are divided into groups, and the features satisfying given constraints are parallel selected. Our framework can not only guide a relatively stable feature sequencing if data perturbation occurs but can also reduce time consumption for feature reduction. The experimental results over 25 UCI data sets with four different ratios of noisy labels demonstrated the superiority of our framework through a comparison with eight state-of-the-art algorithms.

Suggested Citation

  • Zhenyu Yin & Yan Fan & Pingxin Wang & Jianjun Chen, 2023. "Parallel Selector for Feature Reduction," Mathematics, MDPI, vol. 11(9), pages 1-33, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2084-:d:1134610
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    References listed on IDEAS

    as
    1. Wang, Han & Liao, Haitao & Ma, Xiaobing & Bao, Rui, 2021. "Remaining Useful Life Prediction and Optimal Maintenance Time Determination for a Single Unit Using Isotonic Regression and Gamma Process Model," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    2. Tingfeng Wu & Jiachen Fan & Pingxin Wang, 2022. "An Improved Three-Way Clustering Based on Ensemble Strategy," Mathematics, MDPI, vol. 10(9), pages 1-22, April.
    3. Bikram Kar & Bikash Kanti Sarkar & Rajesh Kaluri, 2022. "A Hybrid Feature Reduction Approach for Medical Decision Support System," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-20, September.
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    Cited by:

    1. Zeyuan Fan & Jianjun Chen & Hongyang Cui & Jingjing Song & Taihua Xu, 2024. "Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model," Mathematics, MDPI, vol. 12(10), pages 1-18, May.

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