Skip to main content

Overcome the Sort Problem of Low Discrimination by Interval Fuzzy Number

  • Conference paper
  • First Online:
Innovative Management in Information and Production

Abstract

Purposes: to explore the sort problem if student scores discrimination was too low or the arithmetic average was equal. In advance to explore how to test nonparametric analysis by interval fuzzy scores. Procedures: empirical study samples had three groups which each had eight scores and arithmetic average was equal. Methods: to use the fuzzy theory application to create a new model to solve research purposes. Results: the defuzzification value of interval fuzzy scores could solve the problem of research purposes. The key technologies were the new Definition 2.1 of the interval fuzzy scores as (a, b). In advance to calculate three levels defuzzification sort analysis by Definition 2.2. Therefore, results could apply for the sort problem of the same scores or low discrimination. The key technologies could also solve the sort problem of application for admission when 12-year compulsory education is implemented in Taiwan.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. T.C. Chu, Y.C. Lin, Interval arithmetic based fuzzy TOPSIS mode. Expert Syst. Appl. 36, 10870–10876 (2009)

    Article  Google Scholar 

  2. D. Dubois, H. Fargier, J. Fortin, The empirical variance of a set of fuzzy intervals. Proceedings of the International Conference on Fuzzy Systems, Reno, Nevada, pp. 22–25. (IEEE Press, 2005), pp. 885–890

    Google Scholar 

  3. Z.P. Fan, An approach to solve group-decision-making problems with ordinal interval numbers. IEEE Trans. Syst. Man Cybern. B 40(5), 1413–1423 (2010)

    Article  Google Scholar 

  4. J. Harloff, Extracting cover sets from free fuzzy sorting data. Qual. Quan. 45, 1445–1457 (2011). doi: 10.1007/s11135-011-9497-y

    Article  Google Scholar 

  5. T.H. Hsu, T.N.Tsai, P.L.Chiang, Selection of the optimum promotion mix by integrating, a fuzzy linguistic decision model with genetic algorithms. Inform. Sci. 179(1–2), 41–52 (2009)

    Article  Google Scholar 

  6. N. Hung, K. Vladik, B. Wu, X. Gang, Computing statistics under interval and fuzzy uncertainty. Studies in Computational Intelligence (Springer, Berlin, 2011)

    Google Scholar 

  7. J. Lee, H. Lee, Comparison of fuzzy values on a continuous domain. Fuzzy Sets Syst. 118, 419 (2001)

    Article  MATH  Google Scholar 

  8. C.C. Lin, A.P. Chen, Fuzzy discriminate analysis with outlier detection by genetic algorithm. Comput. Oper. Res. 31(6), 877 (2004)

    MATH  Google Scholar 

  9. Y. Lin, M. Yi, B. Wu, Fuzzy classification analysis of rules usage on probability reasoning test with multiple raw rule score. Educ. Tech. 2, 54–59 (2006)

    Google Scholar 

  10. H. Liu, B. Wu, M. Liu, Investors preference order of fuzzy numbers. Comput. Math. Appl. 55, 2623–2630 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  11. H. Nguyen, B. Wu, Fundamentals of Statistics with Fuzzy Data (Springer, Heidelberg, 2006)

    MATH  Google Scholar 

  12. N. Ravi, V. Shankar, K. Sireesha, S. Rao, N. Vani, Fuzzy Critical Path method based on metric distance ranking of fuzzy numbers. Int. J. Math. Anal. 4(20), 995–1006 (2010)

    MATH  Google Scholar 

  13. A.Sengupta, T.K. Pal, On comparing interval numbers. Eur. J. Oper. Res. 127, 28 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  14. A. Suleman, F. Suleman, Ranking by competence using a fuzzy approach. Qual. Quant. 46, 323–339 (2012). doi: 10.1007/s11135-010-9357-1

    Article  Google Scholar 

  15. M. Sun, B. Wu, New statistical approaches for fuzzy data. Int. J. Uncertain. Fuzz. Knowledge-based Syst. 15(2), 89–106 (2007)

    Article  MathSciNet  Google Scholar 

  16. T.C. Wang, Y.H. Chen, Incomplete fuzzy Linguistic preference relations under uncertain environments. Inform. Fusion 11(2), 201–207 (2010)

    Article  Google Scholar 

  17. B.L. Wu, Y.H. Lin, The introduction of fuzzy mode and its applications. Inform. Stat. Meas. 47, 23–27 (2002)

    Google Scholar 

  18. R.R. Yager, M. Detyniecki, B. Bouchon-Meunier, A context-dependent method for ordering fuzzy numbers using probabilities. Inf. Sci. 138, 237 (2001)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Berlin Wu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this paper

Cite this paper

Lai, W., Wu, B. (2014). Overcome the Sort Problem of Low Discrimination by Interval Fuzzy Number. In: Watada, J., Xu, B., Wu, B. (eds) Innovative Management in Information and Production. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4857-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-4857-0_3

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-4856-3

  • Online ISBN: 978-1-4614-4857-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics