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A Fuzzy Random Variable Approach to Restructuring of Rough Sets through Statistical Test

  • Conference paper
Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5908))

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Abstract

Usually it is hard to classify the situation where randomness and fuzziness exist simultaneously. This paper presents a method based on fuzzy random variables and statistical t-test to restructure a rough set. The algorithms of rough set and statistical t-test are used to distinguish whether a subset can be classified in the object set or not. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

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References

  1. Liu, Y.-K., Liu, B.: Fuzzy random variable: A scalar expected value operator. Fuzzy Optimization and Decision Making 2(2), 143–160 (2003)

    Article  MathSciNet  Google Scholar 

  2. Liu, B.: Uncertainty Theory, 2nd edn. Springer, Berlin (2007)

    MATH  Google Scholar 

  3. Liu, B., Liu, Y.K.: Expected value of fuzzy variable and fuzzy expected value models. IEEE Transaction on Fuzzy Systems 10, 445–450 (2002)

    Article  Google Scholar 

  4. Liu, Y.K., Liu, B.: Fuzzy random variable: A scalar expected value operator. Fuzzy Optimization and Decision Making 2, 143–160 (2003)

    Article  MathSciNet  Google Scholar 

  5. Liu, Y.K., Liu, B.: On minimum-risk problems in fuzzy random decision systems. Computers & Operations Research 32, 257–283 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  6. Nahmias, S.: Fuzzy variables. Fuzzy Sets and Systems 1(2), 97–111 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  7. Wang, S., Watada, J.: T-independence condition for fuzzy random vector based on continuous triangular norms. Journal of Uncertain Systems 2, 155–160 (2008)

    Google Scholar 

  8. Wang, S., Watada, J.: Studying distribution functions of fuzzy random variables and its applications to critical value functions. International Journal of Innovative Computing, Information & Control 5, 279–292 (2009)

    Google Scholar 

  9. Watada, J., Wang, S.: Regression model based on fuzzy random variables. In: Rodulf, S. (ed.) Views on Fuzzy Sets and Systems from Different Perspectives, ch. 26. Springer, Berlin (2009)

    Google Scholar 

  10. Watada, J., Wang, S., Pedrycz, W.: Building confidence-interval-based fuzzy random regression models. In: IEEE Transactions on Fuzzy Systems (to be published)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu, Kitakyushu, 808-0135, Fukuoka, Japan

    Junzo Watada, Lee-Chuan Lin, Minji Qiang & Pei-Chun Lin

Authors
  1. Junzo Watada
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  2. Lee-Chuan Lin
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  3. Minji Qiang
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  4. Pei-Chun Lin
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Aided Sciences, Kyushu Institute of Technology, 804-8550, Tobata, Kitakyushu, Japan

    Hiroshi Sakai

  2. Department of Pure Mathematics, University of Calcutta, 35 Ballygunge Circular Road, 700019, Kolkata, India

    Mihir Kumar Chakraborty

  3. Information Technology Department, University of Cairo, 5 Ahmed Zewal St. Orman, Giza, Egypt

    Aboul Ella Hassanien

  4. University of Warsaw & Infobright Inc., Poland

    Dominik Ślęzak

  5. University of Electronic Science and Technology of China, Chengdu, China

    William Zhu

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© 2009 Springer-Verlag Berlin Heidelberg

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Watada, J., Lin, LC., Qiang, M., Lin, PC. (2009). A Fuzzy Random Variable Approach to Restructuring of Rough Sets through Statistical Test. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_33

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  • DOI: https://doi.org/10.1007/978-3-642-10646-0_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10645-3

  • Online ISBN: 978-3-642-10646-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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