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Dominance-Based N-soft Rough Sets

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Learning Technologies and Systems (SETE 2020, ICWL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12511))

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Abstract

In the present paper, a new approach dominance-based N-soft rough sets is being introduced to study roughness through dominance relation and N-soft sets. Moreover, approximation operators and some useful properties with respect to dominance-based N-soft rough approximation space are introduced. Furthermore, a potential application in matching the corresponding courses according to the students’ knowledge structure and knowledge state are presented with decision-making procedures. This algorithm can address the problems of multi-criteria classification in inconsistent ordered information system.

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Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China

    Di Zhang, Qian Wu, Sheng-Nan Cao & Pi-Yu Li

Authors
  1. Di Zhang
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  2. Qian Wu
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  3. Sheng-Nan Cao
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  4. Pi-Yu Li
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Corresponding author

Correspondence to Pi-Yu Li .

Editor information

Editors and Affiliations

  1. Ningbo Institute of Industrial Technology, Ningbo, China

    Chaoyi Pang

  2. College of Computer Science, Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Monash University, Clayton, VIC, Australia

    Guanliang Chen

  4. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania

    Elvira Popescu

  5. Department of Physics, Zhejiang University, Hangzhou, China

    Lu Chen

  6. School of Computer Science, South China Normal University, Guangzhou, China

    Tianyong Hao

  7. Zhejiang University, Ningbo, China

    Bailing Zhang

  8. Universidad Internacional De La Rioja, Logroño, Spain

    Silvia Margarita Baldiris Navarro

  9. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

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Zhang, D., Wu, Q., Cao, SN., Li, PY. (2021). Dominance-Based N-soft Rough Sets. In: Pang, C., et al. Learning Technologies and Systems. SETE ICWL 2020 2020. Lecture Notes in Computer Science(), vol 12511. Springer, Cham. https://doi.org/10.1007/978-3-030-66906-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-66906-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66905-8

  • Online ISBN: 978-3-030-66906-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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