Extensions and Applications of Evidence Theory

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 76)


Although the Dempster-Shafer theory of evidence fits in handling both imprecision and uncertainty very effectively, a large amount of researches have shown its extensions to be needed. The belief function can not handle the issue of comparisons. The orthogonal sum indeed plays a main role in evidential reasoning, but it has been criticised when it is used for combining two largely conflicting pieces of evidence. Also, it can only be used to combine evidence coming from the same frame of discernment.


Mass Function Blood Type Combination Rule Belief Function Evidential Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anand, S. S.; Bell, D. A.; Hughes, J. G. 1996, “EDM: a general framework for data mining based on evidence theory”, Data f4 Knowledge Engineering, 18 (1996), 189–223.CrossRefGoogle Scholar
  2. 2.
    Anand, S. S.; Scotney, B. W.; Tan, M. G.; McClean, S. I.; Bell, D. A.; Hughes, J. G.; Magill, I. C. 1997, “Designing a kernel for data mining”, IEEE Expert, Vol. 12 (1997), No. 2, 65–74.CrossRefGoogle Scholar
  3. 3.
    Bell, D. 1993, “From data properties to evidence”, IEEE Transactions of Knowledge el Data Engineering, Vol. 5, No. 6 (December 1993), 965–969.CrossRefGoogle Scholar
  4. 4.
    Bell, D.; Guan, J.; Lee, S. K. 1996, “Generalized union and project operations for pooling uncertain and imprecise information”, Data ê? Knowledge Engineering, Vol. 18, 89–117.CrossRefGoogle Scholar
  5. 5.
    Dubois, D.; Prade, H. 1986, Possibility theory: An Approach to Computerized Processing of Uncertainty, ( 1986 ) Plenum Press, New York.Google Scholar
  6. 6.
    Dubois, D.; Prade, H. 1987, “An Approach to Approximate Reasoning Based on the Dempster Rule of Combination”, International Journal of Expert Systems, Vol. 1, No. 1 (1987) 67–85.Google Scholar
  7. 7.
    Dubois, D.; Prade, H. 1990, “Modeling Uncertain and vague knowledge in Possibility and Evidence Theories”, Uncertainty in Artificial Intelligence 4 (1990) 303–318.Google Scholar
  8. 8.
    Guan, J.; Bell, D. 1991–2, Evidence Theory and its Applications, Vol.1–2, (19912) North-Holland.Google Scholar
  9. 9.
    Hau, H. Y.; Kashyap, R. L. 1990, Belief combination and propagation in a lattice-structured inference network, IEEE Trans. on Systems, Man, and Cybernatics, Vol. 20 (1990), No. 1, 45–57.CrossRefGoogle Scholar
  10. 10.
    Lee, S.K. 1992a, “Imprecise and Uncertain Information in Databases: An Evidential Approach”, In Proc. 8th Int. Conf. Data Engineering (1992) 614–621.Google Scholar
  11. 11.
    Lee, S. K. 1992b, “A extended relational database model for uncertain and imprecise information”, Proceedings of the 18th VLDB Conference, Vancouver, British Columbia, Canada, 1992, 211–220.Google Scholar
  12. 12.
    Lim, E. P.; Srivastava, J.; Shekhar, S. 1996, “An evidentail reasoning approach to attribute value conflict resolution in database integration”, IEEE Transactions of Knowledge & Data Engineering, Vol. 8, No. 6 (October 1996), 707–722.Google Scholar
  13. 13.
    Shafer, G. 1976, A Mathematical theory of evidence. Princeton, NJ: Princeton Univ. Press, (1976).Google Scholar
  14. 14.
    Yager, R. R. 1982, “A new approach to the summarization of data”, in Information Sciences, 28, 69–86.Google Scholar
  15. 15.
    Yager, R. R. 1987, “On the Dempster-Shafer framework and new combination rules”, in Information Sciences, 41, 93–137.Google Scholar
  16. 16.
    Yager, R. R. 1991, “On linguistic summaries of data”, in Knowledge Discovery in Databases G. Piatetsky-Shapiro and B. Frawley, Eds., MIT Press, Cambridge, MA, 347–363.Google Scholar
  17. 17.
    Yager, R. R. 1996, “Database discovery using fuzzy sets”, in International Journal of Intelligent Systems, Vol. 11, 691–712.Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2001

Authors and Affiliations

  • Di Cai
    • 1
  1. 1.Department of Computing ScienceUniversity of GlasgowGlasgowScotland, UK

Personalised recommendations