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Triple-G: a new MGRS and attribute reduction

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Abstract

Different from classical rough set, Multigranulation Rough Set (MGRS) is frequently designed for approximating target through using multiple results of information granulation. Presently, though many forms of MGRS have been intensively explored, most of them are constructed based on the homogeneous information granulation with respect to different scales or levels. They lack the multi-view which involves the results of heterogeneous information granulation. To fill such a gap, a Triple-G MGRS is developed. Such a Triple-G is composed of three different heterogeneous information granulations: (1) neiGhborhood based information granulation; (2) Gap based information granulation; (3) Granular ball based information granulation. Neighborhood provides a parameterized mechanism while gap and granular ball offer two representative data-adaptive strategies for performing information granulation. Immediately, both optimistic and pessimistic MGRS can be re-constructed. Furthermore, the problem of attribute reduction is also addressed based on the proposed models. Not only the forward greedy searching is used for deriving the Triple-G MGRS related reducts, but also an attribute grouping based accelerator is reported for further speeding up the process of searching reducts. The experimental results over 20 UCI data sets demonstrate the follows: (1) from the viewpoint of the generalization performance, the reducts obtained by our Triple-G MGRS is superior to those obtained by previous researches; (2) attribute grouping does speed up the process of searching reducts.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (Nos. 62076111, 62006099, 62006128, 61906078), the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX21_3488), the Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province(No. OBDMA202002).

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Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China

    Jing Ba, Keyu Liu, Taihua Xu & Xibei Yang

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Keyu Liu

  3. School of Information Science and Technology, Nantong University, Jiangsu, 226000, China

    Hengrong Ju

  4. School of Computer Science and Technology, Nanjing University, Jiangsu, 210000, China

    Suping Xu

  5. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6R 2V4, Canada

    Suping Xu

  6. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhoushan, 316022, Zhejiang, China

    Xibei Yang

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  1. Jing Ba
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Correspondence to Xibei Yang.

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Ba, J., Liu, K., Ju, H. et al. Triple-G: a new MGRS and attribute reduction. Int. J. Mach. Learn. & Cyber. 13, 337–356 (2022). https://doi.org/10.1007/s13042-021-01404-7

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