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Multi-decision-makers-based Monotonic Variable Consistency Rough Set Approach with Multiple Attributes and Criteria

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

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Abstract

The paper separates decision expression system into three parts: the relation system, the decision-making system and the causal system by the perspective of Pansystems theory. In these three separated systems, the extended approach involves multiple types of attributes and many decision-makers, and it aims at modelling data expressed by monotonic variable consistency measures. Furthermore, the two referred thresholds, according to Bayes decision procedure that is applied by Decision Theoretic Rough Set, can be calculated directly. So the paper proposes Multi-decision-makers-based Monotonic Variable Consistency Rough Set Approach with Multiple Attributes and Criteria, and its properties are proposed and proved.

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Acknowledgement

The paper is supported by the Fundamental Research Funds for the Central Universities(lzujbky-2012-43). The authors thank valued amendments which are raised by Professor Yongli Li.

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Authors and Affiliations

  1. School of Information Science and Engineering, LanZhou University, Lanzhou, 730000, People’s Republic of China

    Wenbin Pei, He Lin & Li Li

Authors
  1. Wenbin Pei
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  2. He Lin
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  3. Li Li
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Corresponding author

Correspondence to He Lin .

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Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, China

    Qinghua Hu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

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Pei, W., Lin, H., Li, L. (2015). Multi-decision-makers-based Monotonic Variable Consistency Rough Set Approach with Multiple Attributes and Criteria. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_36

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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