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Transactions on Rough Sets XVIII pp 52–68Cite as

Variable Precision Multigranulation Rough Set and Attributes Reduction

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Part of the book series: Lecture Notes in Computer Science ((TRS,volume 8449))

Abstract

Multigranulation rough set is a new expansion of the classical rough set since the former uses a family of the binary relations instead of single one for the constructing of approximations. In this paper, the model of the variable precision rough set is introduced into the multigranulation environment and then the concept of the variable precision multigranulation rough set is proposed, which include optimistic and pessimistic cases. Not only basic properties of variable precision multigranulation rough set are investigated, but also the relationships among variable precision rough set, multigranulation rough set and variable precision multigranulation rough set are examined. Finally, a heuristic algorithm is presented for computing reducts of variable precision multigranulation rough set, it is also tested on five UCI data sets.

This is an extended version of the paper presented at the 2012 Joint Rough Set Symposium.

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Acknowledgment

This work is supported by the Natural Science Foundation of China (Nos. 61100116, 61272419, 61305058), the Natural Science Foundation of Jiangsu Province of China (Nos. BK2011492, BK2012700, BK20130471), Qing Lan Project of Jiangsu Province of China, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education (No. 30920130122005), Key Laboratory of Artificial Intelligence of Sichuan Province (No. 2013RYJ03), Natural Science Foundation of Jiangsu Higher Education Institutions of China (Nos. 13KJB520003, 13KJD520008), Postgraduate Innovation Foundation of University in Jiangsu Province of China under Grant No. CXLX13_707.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, People’s Republic of China

    Hengrong Ju, Xibei Yang, Huili Dou & Jingjing Song

  2. Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information, Nanjing University of Science and Technology, Ministry of Education, Nanjing, 210094, People’s Republic of China

    Hengrong Ju, Huili Dou & Jingjing Song

  3. Artificial Intelligence Key Laboratory of Sichuan Province, Zigong, 643000, People’s Republic of China

    Xibei Yang

  4. School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Xibei Yang

Authors
  1. Hengrong Ju
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  2. Xibei Yang
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  3. Huili Dou
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  4. Jingjing Song
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Corresponding author

Correspondence to Xibei Yang .

Editor information

Editors and Affiliations

  1. ECE Department Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB, Canada

    James F. Peters

  2. University of Warsaw, Warsaw, Poland

    Andrzej Skowron  & Hung Son Nguyen  & 

  3. Southwest Jiaotong University, Sichuan, China

    Tianrui Li  & Yan Yang  & 

  4. University of Regina, Regina, SK, Canada

    JingTao Yao

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Ju, H., Yang, X., Dou, H., Song, J. (2014). Variable Precision Multigranulation Rough Set and Attributes Reduction. In: Peters, J.F., Skowron, A., Li, T., Yang, Y., Yao, J., Nguyen, H.S. (eds) Transactions on Rough Sets XVIII. Lecture Notes in Computer Science(), vol 8449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44680-5_4

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  • DOI: https://doi.org/10.1007/978-3-662-44680-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44679-9

  • Online ISBN: 978-3-662-44680-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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