Nothing Special   »   [go: up one dir, main page]

Skip to main content

Medical Diagnosis with Type-2 Fuzzy Decision Trees

  • Chapter
Computers in Medical Activity

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 65))

Abstract

In this paper, we propose type-2 fuzzy decision trees in application to medical diagnosis. This means that attribute values employed in the tree structures may be characterized by type-2 fuzzy sets. Three medical benchmark data sets, available on the Internet, have been used to illustrate results of diagnosis obtained by this method.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook
USD 15.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Adamo, J.M.: Fuzzy decision trees. Fuzzy Sets and Systems 4, 207–219 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  2. Bartczuk, Ł., Rutkowska, D.: A new version of the fuzzy-ID3 algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 1060–1070. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Bartczuk, Ł., Rutkowska, D.: Fuzzy decision trees of type-2. In: Some Aspects of Computer Science. EXIT Academic Publishing House, Warsaw (2007) (in Polish)

    Google Scholar 

  4. Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases. University of California, Dept. of Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html

  5. Canfora, G., Troiano, L.: Fuzzy ordering of fuzzy numbers. In: Proc. Fuzz-IEEE, Budapest, pp. 669–674 (2004)

    Google Scholar 

  6. Chang, W.: Ranking of fuzzy utilities with triangular membership functions. In: Proc. Intern. Conference on Policy Analysis and Systems, pp. 263–272 (1981)

    Google Scholar 

  7. Dong, M.: Look-ahead based fuzzy decision tree induction. IEEE Trans. Fuzzy Systems 9(3), 461–468 (2001)

    Article  Google Scholar 

  8. Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, San Diego (1980)

    MATH  Google Scholar 

  9. Hwang, C., Rhee, F.: Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means. IEEE Trans. Fuzzy Systems 15(1), 107–120 (2007)

    Article  Google Scholar 

  10. Rhee, F.: Uncertain Fuzzy clustering: insights and recommendations. IEEE Comp. Intelligence Magazine 2(1), 44–56 (2007)

    Article  Google Scholar 

  11. Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Trans. Systems Man Cybern. 28(3), 1–14 (1998)

    Google Scholar 

  12. Liang, Q., Mendel, J.M.: Interval type-2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Systems 8, 535–550 (2000)

    Article  Google Scholar 

  13. Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems - Introduction and New Directions. Prentice Hall PTR, Englewood Cliffs (2001)

    MATH  Google Scholar 

  14. Mendel, J.M.: Computing with words, when words can mean different things to different people. In: Proc. Intern. ICSC Congress on Computational Intelligence, Rochester, New York (1999)

    Google Scholar 

  15. Mendel, J.M., John, R.I.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Systems 10, 117–127 (2002)

    Article  Google Scholar 

  16. Moore, R.E.: Interval Analysis. Prentice-Hall, Englewood Cliffs (1966)

    MATH  Google Scholar 

  17. Olaru, C., Wehenkel, L.: A complete fuzzy decision tree technique. Fuzzy Sets and Systems 138, 221–254 (2003)

    Article  MathSciNet  Google Scholar 

  18. Piegat, A.: Modeling and Fuzzy Control. EXIT Academic Publishing House, Warsaw (1999) (in Polish)

    Google Scholar 

  19. Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)

    Google Scholar 

  20. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Inc., Los Altos (1993)

    Google Scholar 

  21. Quinlan, J.R.: Learning with continuous classes. In: Proc. 5th Australian Joint Conference on Artificial Intelligence, pp. 343–348. World Scientific, Singapore (1992)

    Google Scholar 

  22. Rutkowska, D.: Neuro-Fuzzy Architectures and Hybrid Learning. Physica-Verlag, Springer-Verlag Company, Heidelberg (2002)

    MATH  Google Scholar 

  23. Rutkowski, L.: Methods and Techniques of Artificial Intelligence. PWN, Warsaw (2005) (in Polish)

    Google Scholar 

  24. Wang, X., Borgelt, C.: Information measures in fuzzy decision trees. In: Proc. IEEE Intern. Conference on Fuzzy Systems, Budapest, vol. 1, pp. 85–90 (2004)

    Google Scholar 

  25. Yager, R.R.: Ranking fuzzy subsets over the unit interval. In: Proc. CDC, pp. 1435–1437 (1978)

    Google Scholar 

  26. Yager, R.R.: On choosing between fuzzy subsets. Kybernetes 9, 151–154 (1980)

    Article  MATH  Google Scholar 

  27. Yager, R.R.: Procedure for ordering fuzzy sets of the unit interval. Inform. Sci. 24, 143–161 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  28. Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy Sets and Systems 69, 125–139 (1995)

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  30. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Information Science, Part I 8, 199–249, Part II 8, 301–357, Part III 9, 43–80 (1975)

    Google Scholar 

  31. Zimmermann, H.-J.: Fuzzy Set Theory. Kluwer Academic Publishers, Boston (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bartczuk, Ł., Rutkowska, D. (2009). Medical Diagnosis with Type-2 Fuzzy Decision Trees. In: Kącki, E., Rudnicki, M., Stempczyńska, J. (eds) Computers in Medical Activity. Advances in Intelligent and Soft Computing, vol 65. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04462-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04462-5_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04461-8

  • Online ISBN: 978-3-642-04462-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics