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Multi-source Information Fusion Using Measure Representations

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On Logical, Algebraic, and Probabilistic Aspects of Fuzzy Set Theory

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 336))

Abstract

We first look at the issue of representing information about an uncertain variable using a measure. We focus on some notable measures that can be used. We discuss the role of aggregation functions in the task of combining measures to form new measures. We look at this in the framework of multi-source information fusion. We focus on the fusion of probabilistic and possibilistic information and discuss its role in hard-soft information fusion. We look at some characterizing features associated with measures used to represent uncertain values of variables. We discuss the concepts of assurance and opportunity that play a role in the process of answering questions using information obtained from a measure.

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References

  1. Beliakov, G., Pradera, A., Calvo, T.: Aggregation Functions: A Guide for Practitioners. Springer, Heidelberg (2007)

    Google Scholar 

  2. Dubois, D., Prade, H.: Necessity measures and the resolution principle. IEEE Trans. Syst. Man Cybern. 17, 474–478 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  3. Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press, New York (1988)

    Book  MATH  Google Scholar 

  4. Dubois, D., Prade, H.: Formal representations of uncertainty. In: Bouyssou, D., Dubois, D., Pirlot, M., Prade, H. (eds.) Decision Making Process: Concepts and Methods. Wiley, Hoboken (2010)

    Google Scholar 

  5. Grabisch, M., Marichal, J.-L., Mesiar, R., Pap, E.: Aggregation Functions. Cambridge University Press, Cambridge (2009)

    Book  MATH  Google Scholar 

  6. Klement, E.P.: A theory of fuzzy measures: a survey. In: Gupta, M.M., Sanchez, E. (eds.) Fuzzy Information and Decision Processes, pp. 59–66. North-Holland, Amsterdam (1982)

    Google Scholar 

  7. Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic Publishers, Dordrecht (2000)

    Book  MATH  Google Scholar 

  8. Klir, G.J.: Uncertainty and Information. Wiley, New York (2006)

    MATH  Google Scholar 

  9. Liu, L., Yager, R.R.: Classic works of the Dempster-Shafer theory of belief functions: an introduction. In: Yager, R.R., Liu, L. (eds.) Classic Works of the Dempster-Shafer Theory of Belief Functions, pp. 1–34. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  10. Llinas, J., Nagi, R., Hall, D.L., Lavery, J.: A multi-disciplinary university research initiative in hard and soft information fusion: Overview, research strategies and initial results. In: Proceedings of the 13th International Conference on Information Fusion (Fusion 2010). Edinburgh, UK, Unpaginated (2010)

    Google Scholar 

  11. Shapley, L.S.: A value for \(n\)-person games. In: Kuhn, H.W., Tucker, A.W. (eds.) Contributions to Game Theory, pp. 307–317. Princeton University Press, Princeton (1953)

    Google Scholar 

  12. Sugeno, M.: Fuzzy measures and fuzzy integrals: a survey. In: Gupta, M.M., Saridis, G.N., Gaines, B.R. (eds.) Fuzzy Automata and Decision Process, pp. 89–102. North-Holland, Amsterdam (1997)

    Google Scholar 

  13. Walley, P., Fine, T.: Toward a frequentist theory of upper and lower probability. Ann. Stat. 10, 741–761 (1982)

    Google Scholar 

  14. Wang, Z., Yang, R., Leung, K.S.: Nonlinear Integrals and Their Applications in Data Mining. World Scientific, Singapore (2010)

    MATH  Google Scholar 

  15. Yager, R.R.: On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. Syst. Man Cybern. 18, 183–190 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  16. Yager, R.R.: On the entropy of fuzzy measures. IEEE Trans. Fuzzy Sets Syst. 8, 453–461 (2000)

    Article  Google Scholar 

  17. Yager, R.R.: Measuring the information and character of a fuzzy measure. In: Proceedings of the Joint 9th IFSA World Congress and the 20th NAFIPS International Conference, pp. 1718–1722. Vancouver (2001)

    Google Scholar 

  18. Yager, R.R.: On the cardinality index and attitudinal character of fuzzy measures. Int. J. Gen. Syst. 31, 303–329 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  19. Yager, R.R.: A measure based approach to the fusion of possibilistic and probabilistic uncertainty. Fuzzy Optim. Decis. Making 10, 91–113 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  20. Yager, R.R.: Measures of assurance and opportunity in modeling uncertain information. Int. J. Intell. Syst. 27, 776–796 (2012)

    Article  Google Scholar 

  21. Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zadeh, L.A.: From computing with numbers to computing with words–From manipulation of measurements to manipulations of perceptions. IEEE Trans. Circuits Syst. 45, 105–119 (1999)

    Article  MathSciNet  Google Scholar 

  23. Zadeh, L.A.: Generalized theory of uncertainty (GTU)-principal concepts and ideas. Comput. Stat. Data Anal. 51, 15–46 (2006)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Machine Intelligence Institute, Iona College, New Rochelle, NY, 10801, USA

    Ronald R. Yager

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  1. Ronald R. Yager
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Correspondence to Ronald R. Yager .

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Editors and Affiliations

  1. Dept of Knowledge-Based Math. Systems, Johannes Kepler University Linz, Linz, Austria

    Susanne Saminger-Platz

  2. Dept. of Mathematics & Descriptive Geo., Slovak University of Technology, BRATISLAVA, Slovakia

    Radko Mesiar

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Yager, R.R. (2016). Multi-source Information Fusion Using Measure Representations. In: Saminger-Platz, S., Mesiar, R. (eds) On Logical, Algebraic, and Probabilistic Aspects of Fuzzy Set Theory. Studies in Fuzziness and Soft Computing, vol 336. Springer, Cham. https://doi.org/10.1007/978-3-319-28808-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-28808-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28807-9

  • Online ISBN: 978-3-319-28808-6

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