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Classification and Decision Based on Parallel Reducts and F-Rough Sets

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Rough Sets and Knowledge Technology (RSKT 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7414))

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Abstract

F-rough sets are new rough set model, which is consistent with parallel reducts. In this paper, the methods of classification (decision) with parallel reducts and F-rough sets are discussed. Unlike Pawlak rough sets or other rough set models, there may be many benchmarks for classifying(deciding). Three strategies for classifying(deciding) are proposed, including specific decision subsystem, decision subsystem selected randomly and deciding by a majority vote.

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Author information

Authors and Affiliations

  1. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China

    Dayong Deng, Lin Chen & Dianxun Yan

Authors
  1. Dayong Deng
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  2. Lin Chen
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  3. Dianxun Yan
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Editor information

Editors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, 610031, Chengdu, P.R. China

    Tianrui Li

  2. Institute of Mathematics, The University of Warsaw, ul. Banacha 2, 02-097, Warsaw, Poland

    Hung Son Nguyen

  3. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Guoyin Wang

  4. Department of Electrical Engineering and Computer Science, University of Kansas, 66045 - 7621, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  5. Department of Computing and Software, McMaster University, L8S 4K1, Hamilton, Ontario, Canada

    Ryszard Janicki

  6. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  7. Software School, Dalian University of Technology, Dalian, China

    Hong Yu

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© 2012 Springer-Verlag Berlin Heidelberg

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Deng, D., Chen, L., Yan, D. (2012). Classification and Decision Based on Parallel Reducts and F-Rough Sets. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_15

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  • DOI: https://doi.org/10.1007/978-3-642-31900-6_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31899-3

  • Online ISBN: 978-3-642-31900-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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