Advertisement

European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty

ECSQARU 2015: Symbolic and Quantitative Approaches to Reasoning with Uncertainty pp 301-311 | Cite as

Learning Structure in Evidential Networks from Evidential DataBases

  • Narjes Ben HarizEmail author
  • Boutheina Ben Yaghlane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9161)

Abstract

Evidential networks have gained a growing interest as a good tool fusing belief function theory and graph theory to analyze complex systems with uncertain data. The graphical structure of these models is not always clear, it can be fixed by experts or constructed from existing data. The main issue of this paper is how to extract the graphical structure of an evidential network from imperfect data stored in evidential databases.

References

  1. 1.
    Bach Tobji, M.A., Ben Yaghlane, B., Mellouli, K.: A new algorithm for mining frequent itemsets from evidential databases. In: Proceedings of Information Processing and Management of Uncertainty (IPMU 2008), pp. 1535–1542, Malaga (2008)Google Scholar
  2. 2.
    Ben Hariz, N., Ben Yaghlane, B.: Learning parameters in directed evidential networks with conditional belief functions. In: Cuzzolin, F. (ed.) BELIEF 2014. LNCS, vol. 8764, pp. 294–303. Springer, Heidelberg (2014) Google Scholar
  3. 3.
    Ben Yaghlane, B.: Uncertainty Representation and Reasoning in Directed Evidential Networks. P.hD. thesis, Institut Supérieur de Gestion de Tunis (2002)Google Scholar
  4. 4.
    Ben Yaghlane, B., Mellouli, K.: Inference in directed evidential networks based on the transferable belief model. IJAR 48(2), 399–418 (2008)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Chickering, D.: Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, 507–554 (2002)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Ser. B 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Denœux, T.: Maximum likelihood estimation from uncertain data in the belief function framework. Knowl. Data Eng. 25, 113–119 (2013)Google Scholar
  8. 8.
    Hewawasam, K., Premaratne, K., Subasingha, S., Shyu, M.-L.: Rule mining and classifcation in imperfect databases. In: Proceedings of the 8th International Conference on Information Fusion, vol. 1, pp. 661–668, Philadelphia (2005)Google Scholar
  9. 9.
    Jordan, M.: Learning in Graphical Models. Kluwer Academic Publisher, New York (1998) CrossRefGoogle Scholar
  10. 10.
    Krause, P.J.: Learning probabilistic networks. Knowl. Eng. Rev. 13(4), 321–351 (1998)CrossRefGoogle Scholar
  11. 11.
    Lauritzen, S.L., Spiegelhalter, D.J.: Local computation with probabilities and graphical structures and their application to expert systems. J. Roy. Stat. Soc. B 50, 157–224 (1988)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Naim, P., Wuillemin, P.H., Leray, P., Pourret, O., Becker, A.: Réseaux Bayésiens. Eyrolles, Paris (2004)Google Scholar
  13. 13.
    Neapolitan, R.: Learning Bayesian Networks. Prenctice Hall, New York (2003)Google Scholar
  14. 14.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, New York (1988)zbMATHGoogle Scholar
  15. 15.
    Pearl, J., Verma, T.: A theory of inferred causation. In: Allen, J., Fikes, R., Sandewall, E. (eds.) Proceedings of the Second International Conference on Knowledge Representation and Reasoning, pp. 441–452. Morgan Kaufmann, New York (1991)Google Scholar
  16. 16.
    Petitrenaud, S.: Independence tests for uncertain data with a frequentist method. In: Proceedings of the 5th International Conference on Soft Methods in Probability and Statistic (SMPS 2010), pp. 519–526 (2010)Google Scholar
  17. 17.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  18. 18.
    Smets, P.: Jeffrey’s rule of conditioning generalized to belief functions. In: Proceedings of the Ninth international conference on Uncertainty in artificial intelligence (UAI 1993), pp. 500–505, Washington (1993)Google Scholar
  19. 19.
    Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66, 191–234 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. Springer-Verlag, New York (2000)zbMATHGoogle Scholar
  21. 21.
    Tang, Y., Zheng, J.: Dempster conditioning and conditional independence in evidence theory. In: Australian Conference on Artificial Intelligence, vol. 3809, pp. 822–825, Sydney (2005)Google Scholar
  22. 22.
    Verma, T., Pearl, J.: Equivalence and synthesis of causal models. In: Proceedings of Sixth Conference on Uncertainty in Artifcial Intelligence, pp. 220–227 (1990)Google Scholar
  23. 23.
    Xu, H., Smets, Ph.: Evidential reasoning with conditional belief functions. In: Heckerman, D., et al. (eds.) Proceedings of Uncertainty in Artificial Intelligence (UAI 1994), pp. 598–606, Seattle (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.LARODEC LaboratoryInstitut Supérieur de Gestion de TunisLe BardoTunisia

Personalised recommendations