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Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation

Received: 9 August 2018     Published: 10 August 2018
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Abstract

In incomplete information system, combining the advantages of maximal consistent relation and multiparticle theory, this paper proposed the multi-granulation decision–theoretic rough set based on maximal consistent relation based on consistent relation. Firstly, this paper define variable precision maximal consistent relation and dual-variable maximal consistent relation respectively for two kinds of incomplete information systems with different value types. Then, this paper establish optimistic and pessimistic multi-granulation decision–theoretic rough set model by replacing the equivalence relation with the maximal consistent relation in multi-granulation decision-theoretic rough set. Finally, it is proved that the maximum compatible relationship can improve the classification accuracy effectively based on model of optimistic maximal consistent relation, and this paper prove that the robustness of the classification can be improved by multiple classification thresholds at Multi-granulation.

Published in Science Discovery (Volume 6, Issue 4)
DOI 10.11648/j.sd.20180604.20
Page(s) 290-297
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Maximal Consistent Relation, Multi-Granulation, Decision–Theoretic Rough Set, Classification Accuracy

References
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  • APA Style

    Fan Bingbing, Li Jin, Chen Xicheng, Liu Mengbo, Gu Jinghao, et al. (2018). Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation. Science Discovery, 6(4), 290-297. https://doi.org/10.11648/j.sd.20180604.20

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    ACS Style

    Fan Bingbing; Li Jin; Chen Xicheng; Liu Mengbo; Gu Jinghao, et al. Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation. Sci. Discov. 2018, 6(4), 290-297. doi: 10.11648/j.sd.20180604.20

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    AMA Style

    Fan Bingbing, Li Jin, Chen Xicheng, Liu Mengbo, Gu Jinghao, et al. Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation. Sci Discov. 2018;6(4):290-297. doi: 10.11648/j.sd.20180604.20

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  • @article{10.11648/j.sd.20180604.20,
      author = {Fan Bingbing and Li Jin and Chen Xicheng and Liu Mengbo and Gu Jinghao and Liu Ming},
      title = {Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation},
      journal = {Science Discovery},
      volume = {6},
      number = {4},
      pages = {290-297},
      doi = {10.11648/j.sd.20180604.20},
      url = {https://doi.org/10.11648/j.sd.20180604.20},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20180604.20},
      abstract = {In incomplete information system, combining the advantages of maximal consistent relation and multiparticle theory, this paper proposed the multi-granulation decision–theoretic rough set based on maximal consistent relation based on consistent relation. Firstly, this paper define variable precision maximal consistent relation and dual-variable maximal consistent relation respectively for two kinds of incomplete information systems with different value types. Then, this paper establish optimistic and pessimistic multi-granulation decision–theoretic rough set model by replacing the equivalence relation with the maximal consistent relation in multi-granulation decision-theoretic rough set. Finally, it is proved that the maximum compatible relationship can improve the classification accuracy effectively based on model of optimistic maximal consistent relation, and this paper prove that the robustness of the classification can be improved by multiple classification thresholds at Multi-granulation.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Multi-Granulation Decision-Theoretic Rough Set Based on Maximal Consistent Relation
    AU  - Fan Bingbing
    AU  - Li Jin
    AU  - Chen Xicheng
    AU  - Liu Mengbo
    AU  - Gu Jinghao
    AU  - Liu Ming
    Y1  - 2018/08/10
    PY  - 2018
    N1  - https://doi.org/10.11648/j.sd.20180604.20
    DO  - 10.11648/j.sd.20180604.20
    T2  - Science Discovery
    JF  - Science Discovery
    JO  - Science Discovery
    SP  - 290
    EP  - 297
    PB  - Science Publishing Group
    SN  - 2331-0650
    UR  - https://doi.org/10.11648/j.sd.20180604.20
    AB  - In incomplete information system, combining the advantages of maximal consistent relation and multiparticle theory, this paper proposed the multi-granulation decision–theoretic rough set based on maximal consistent relation based on consistent relation. Firstly, this paper define variable precision maximal consistent relation and dual-variable maximal consistent relation respectively for two kinds of incomplete information systems with different value types. Then, this paper establish optimistic and pessimistic multi-granulation decision–theoretic rough set model by replacing the equivalence relation with the maximal consistent relation in multi-granulation decision-theoretic rough set. Finally, it is proved that the maximum compatible relationship can improve the classification accuracy effectively based on model of optimistic maximal consistent relation, and this paper prove that the robustness of the classification can be improved by multiple classification thresholds at Multi-granulation.
    VL  - 6
    IS  - 4
    ER  - 

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Author Information
  • Graduate College Air Force Engineering University, Xi’an, China

  • Graduate College Air Force Engineering University, Xi’an, China

  • Graduate College Air Force Engineering University, Xi’an, China

  • Graduate College Air Force Engineering University, Xi’an, China

  • Graduate College Air Force Engineering University, Xi’an, China

  • Graduate College Air Force Engineering University, Xi’an, China

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