Skip to main content
Springer Nature Link
Log in

Fuzzy Rank Linear Regression Model

  • Conference paper
AI 2009: Advances in Artificial Intelligence (AI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5866))

Included in the following conference series:

  • 1671 Accesses

Abstract

In this paper, we construct a fuzzy rank linear regression model using the rank transform (RT) method and least absolute deviation (LAD) method based on the α-level sets of fuzzy numbers. The rank transform method is known to be efficient when the error distribution does not satisfy the conditions for normality and the method is not sensitive to outliers in the regression analysis. Some examples are given to compare the effectiveness of the proposed method with other existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Choi, S.H., Buckley, J.J.: Fuzzy regression using least absolute deviation estimators. Soft Computing 12, 257–263 (2008)

    Article  Google Scholar 

  2. Diamond, P.: Fuzzy Least Squares. Inform. Sci. 46, 141–157 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  3. Diamond, P., Tanaka, H.: Fuzzy regression analysis. Fuzzy sets in decision analysis, operations research and statistics. Kluwer Academic Publishers, Norwell (1999)

    Google Scholar 

  4. Headrick, T.C., Rotou, O.: An investigation of the rank transformation in multiple regression. Computational Statistics and Data Analysis 38, 203–215 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  5. Iman, R.L., Conover, W.J.: The use of the rank transform in regression. Technomerics 21, 499–509 (1979)

    Article  Google Scholar 

  6. Kao, C., Chyu, C.: A fuzzy linear regression model with better explanatory power. Fuzzy Sets and Systems 126, 401–409 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  7. Kao, C., Chyu, C.: Least-squares estimates in fuzzy regression analysis. European J. of Operational Research 148, 426–435 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  8. Kao, C., Lin, P.: Entropy for fuzzy regression analysis. Int. Journal of Sys. Sci. 36, 869–876 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  9. Kim, B., Bishu, R.R.: Evaluation of fuzzy linear regression models by comparing membership functions. Fuzzy Sets and Systems 100, 343–352 (1998)

    Article  Google Scholar 

  10. Kim, H.K., Yoon, J.H., Li, Y.: Asymptotic properties of least squares estimation with fuzzy observations. Inform. Sci. 178, 439–451 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  11. Nasrabadi, M.M., Nasrabadi, E.: A mathematical programming approach to fuzzy linear regression analysis. Applied Mathematical and Computation 155, 873–881 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  12. Sakawa, M., Yano, H.: Multiobjective fuzzy linear regression analysis for fuzzy input-output data. Fuzzy Sets and Systems 47, 173–181 (1992)

    Article  MATH  Google Scholar 

  13. Tanaka, H., Hayashi, I., Watada, J.: Possibilistic linear regression analysis for fuzzy data. European Journal of Operational Research 40, 389–396 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  14. Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Trans. Syst., Man Cybernet. 12, 903–907 (1982)

    Article  MATH  Google Scholar 

  15. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Economics, Yonsei University, Seoul, 120-749, South Korea

    Jin Hee Yoon

  2. School of Liberal Arts and Sciences, Korea Aerospace University, Koyang, 412-791, South Korea

    Seung Hoe Choi

Authors
  1. Jin Hee Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seung Hoe Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Clayton School of Information Technology, Monash University, 3800, Clayton, VIC, Australia

    Ann Nicholson

  2. School of Computer Science and Information Technology, RMIT University, 3001, Melbourne, VIC, Australia

    Xiaodong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yoon, J.H., Choi, S.H. (2009). Fuzzy Rank Linear Regression Model. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10439-8_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10438-1

  • Online ISBN: 978-3-642-10439-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation