Advertisement

Imperfect Pattern Recognition Using the Fuzzy Measure Theory

  • Anas Dahabiah
  • John Puentes
  • Basel Solaiman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

This paper aims to provide a unified framework to deal with information imperfection and heterogeneity using possibility theory, in addition to information conflict and scarcity using Dempster-Shafer theory in order to classify imperfectly-described medical images. The proposed method is very robust and general. It can be applied without modification to any other database.

Keywords

pattern recognition possibility theory Dempster-Shafer theory similarity measuring 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Corter, J.E.: Tree Models of Similarity and Association. In: Quantitative Applications in the Social Sciences, ch. 3, 4. Sage, Thousand Oaks (1996)Google Scholar
  2. 2.
    Mauris, G., Dubois, D., Foulloy, L., Prade, H.: Probability-possibility transformations, triangular fuzzy sets and probabilistic inequalities. Reliable Computing, 273–297 (2004)Google Scholar
  3. 3.
    Dahabiah, A., Puentes, J., Solaiman, B.: Digestive database evidential clustering based on possibility theory. WSEAS trans. on systems 5(9), 239–248 (2008)Google Scholar
  4. 4.
    Denoeux, T.: A neural network classifier based on dempster-shafer theory. IEEE trans. on Systems, Man, and Cybernetics 30, 131–150 (2000)CrossRefGoogle Scholar
  5. 5.
    Denoeux, T.: Evclus: Evidential clustering of proximity data. IEEE trans. on Systems, Man, and Cybernetics 34, 95–109 (2004)CrossRefGoogle Scholar
  6. 6.
    Armstrong, P.I., et al.: Circular unidimensional scaling: A new look at group differences in interest structure. Journal of counseling psychology 50, 297–308 (2003)CrossRefGoogle Scholar
  7. 7.
    Le Guillou, C., Cauvin, J.: From endoscopic imaging and knowledge to semantic formal images. In: Lévy, P.P., Le Grand, B., Poulet, F., Soto, M., Darago, L., Toubiana, L., Vibert, J.-F. (eds.) VIEW 2006. LNCS, vol. 4370, pp. 189–201. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Pecalska, E., Duin, R.: The Dissimilarity Representation for Pattern Recognition, ch. 1, 5, 6, 9. World Scientific Publishing, Singapore (2005)CrossRefGoogle Scholar
  9. 9.
    Ruet, M., Geneste, L.: Search and adaptation in a fuzzy object oriented case base. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 350–364. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Wang, Z., Klir, G.: Fuzzy Measure Theory. Kluwer Academic, Dordrecht (1993)zbMATHGoogle Scholar
  11. 11.
    Zouhal, L.M., Denoeux, T.: An evidence-theoretic k-nn rule with parameter optimization. IEEE trans. on Systems, Man, and Cybernetics 28, 263–271 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Anas Dahabiah
    • 1
  • John Puentes
    • 1
  • Basel Solaiman
    • 1
  1. 1.Image and Information Processing DepartmentTELECOM BretagneBrest Cedex 3France

Personalised recommendations