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International Conference on Hybrid Artificial Intelligence Systems

HAIS 2012: Hybrid Artificial Intelligent Systems pp 369–380Cite as

From Likelihood Uncertainty to Fuzziness: A Possibility-Based Approach for Building Clinical DSSs

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7209))

Abstract

For data classification, in fields like medicine, where vague concepts have to be considered, and where, at the same time, intelligible rules are required, research agrees on utility of fuzzy logic. In this ambit, if statistical information about the problem is known, or can be extracted from data, it can be used to define fuzzy sets and rules. Statistical knowledge can be acquired in terms of probability distributions or likelihood functions. Here, an approach is proposed for the transformation of likelihood functions into fuzzy sets, which considers possibility measure, and different methods arising from this approach are presented. By using real data, a comparison among different methods is performed, based on the analysis of transformation properties and resulting fuzzy sets characteristics. Finally, the best method to be used in the context of clinical decision support systems (DSSs) is chosen.

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References

  1. Zadeh, L.: Fuzzy sets. Inform. Control. 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  2. d’Acierno, A., De Pietro, G., Esposito, M.: Data driven generation of fuzzy systems: An application to breast cancer detection. In: Proc. of CIBB (2010)

    Google Scholar 

  3. Pota, M., Esposito, M., De Pietro, G.: Transformation of probability distribution into a fuzzy set interpretable with likelihood view. In: IEEE 11th International Conference on Hibrid Intelligent Systems (HIS 2011), Malacca, Malaysia, pp. 91–96 (2011)

    Google Scholar 

  4. Dubois, D., Prade, H.: Fuzzy sets and statistical data. European Journal of Operational Research 25, 345–356 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bilgic, T., Türksen, I.B.: Measurement of membership functions: Theoretical and empirical work. In: Dubois, D., Prade, H. (eds.) Handbook of fuzzy sets and systems. Fundamentals of fuzzy sets, vol. 1, pp. 195–232. Kluwer, Dordrecht

    Google Scholar 

  6. Dubois, D., Prade, H.: Fuzzy sets and probability: Misunderstandings, bridges and gaps. In: Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, USA, vol. 2, pp. 1059–1068 (1993)

    Google Scholar 

  7. Zadeh, L.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1, 3–28 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  8. Dubois, D., Foulloy, L., Mauris, G., Prade, H.: Probability-Possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable Computing 10, 273–297 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  9. Yager, R., Kreinovich, V.: Entropy conserving probability transforms and the entaiment principle. Technical Report, MII-2518, Machine Intelligence Institute, Iona College, New Rochelle, NY (2004)

    Google Scholar 

  10. Klir, G.J.: A principle of uncertainty and information invariance. Int. Journal of General Systems 17, 249–275 (1990)

    Article  MATH  Google Scholar 

  11. Dubois, D., Prade, H.: On several representations of uncertain body of evidence. In: Gupta, M.M., Sanchez, E. (eds.) Fuzzy Informatics and Decision Processes, pp. 167–181. North-Holland Pub. (1982)

    Google Scholar 

  12. Geer, J.F., Klir, G.: A mathematical analysis of information-preserving transformations between probabilistic and possibilistic formulations of uncertainty. Int. Journal of General Systems 20, 361–377 (1992)

    Google Scholar 

  13. Shafer, G.: A mathematical theory of evidence. Princeton Univerasity Press, NJ (1976)

    MATH  Google Scholar 

  14. Dubois, D., Prade, H.: Fuzzy sets and systems: Theory and applicatios. Academic Press, New York (1980)

    Google Scholar 

  15. Yamada, K.: Probability-Possibility transformation based on evidence theory. In: Joint 9th IFSA World Congress and 20th NAIPS International Conference, pp. 70–75 (2001)

    Google Scholar 

  16. Pota, M., Esposito, M., De Pietro, G.: Properties Evaluation of an Approach Based on Probability-Possibility Transformation. In: International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2011), December 3-12 (2011) (in press)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for High Performance Computing and Networking, ICAR-CNR, via P. Castellino 111, Naples, 80131, Italy

    Marco Pota, Massimo Esposito & Giuseppe De Pietro

Authors
  1. Marco Pota
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  2. Massimo Esposito
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  3. Giuseppe De Pietro
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Editor information

Editors and Affiliations

  1. Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

  2. VŠB-TU Ostrava 17, Listopadu 15, 70833, Ostrava, Czech Republic

    Václav Snášel

  3. Machine Intelligence Research Labs Machine Intelligence Research Labs(MIR Labs),, Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071, Auburn, Washington, USA

    Ajith Abraham

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of the Basque Country, Pº Manuel Lardizabal 1, 20018, San Sebastian, Spain

    Manuel Graña

  6. Yonsei University, 134 Shinchon-dong, 120-749, Sudaemoon-ku, Seoul, Korea

    Sung-Bae Cho

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© 2012 Springer-Verlag Berlin Heidelberg

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Pota, M., Esposito, M., De Pietro, G. (2012). From Likelihood Uncertainty to Fuzziness: A Possibility-Based Approach for Building Clinical DSSs. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28931-6_36

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  • DOI: https://doi.org/10.1007/978-3-642-28931-6_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28930-9

  • Online ISBN: 978-3-642-28931-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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