Parameterized Imprecise Classification: Elicitation and Assessment

  • Isabela Drummond
  • Sandra Sandri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)


This work is based on classifiers that can yield possibilistic valuations as output. The valuations may have been obtained from a labeled data set either directly as such, by possibilistic classifiers, by transforming the output of probabilistic classifiers or else by adapting prototype-based classifiers in general. Imprecise classifications are elicited from the possibilistic valuations by varying a parameter that makes the overall classification become more or less precise. We introduce some indices to assess the accuracy of the parameterized imprecise classifications and their reliability, thus allowing the user to choose the most suitable level of imprecision and/or uncertainty for a given application.


Discriminant Function Confusion Matrice Possibility Distribution Possibility Theory Accuracy Index 
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.


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  1. 1.
    Bezdek, J., Ehrlich, R., Full, W.: FCM: The fuzzy c-means algorithm. Computers & Geosciences 10(2-3), 191–203 (1984)Google Scholar
  2. 2.
    Cooke, R.M.: Experts in uncertainty. Oxford University Press, Oxford (1991)Google Scholar
  3. 3.
    Drummond, I., Sandri, S.: A clustering-based possibilistic method for image classification. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 454–463. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Drummond, I., Sandri, S.: A clustering-based fuzzy classifier. In: Vuité Congrés Catalá d’Intel.ligência Artificial (CCIA 2005), Artificial Intelligence Research and Development and Frontiers in Artificial Intelligence and Application, Sardegna, Italy, October 2005, vol. 131, pp. 247–254. IOS Press, Berlin (2005)Google Scholar
  5. 5.
    Dubois, D., Prade, H.: Possibility Theory. Plenum Press, New York (1988)MATHGoogle Scholar
  6. 6.
    Dubois, D., Prade, H., Sandri, S.: On possibility/probability transformations. In: Lowen, R., Roubens, M. (eds.) Fuzzy logic:state of the art, pp. 103–112. Kluwer, Dordrecht (1993)Google Scholar
  7. 7.
    Kuncheva, L.I.: Fuzzy classifier design. Springer, Heidelberg (2000)MATHGoogle Scholar
  8. 8.
    Kuncheva, L.I.: Combining pattern classifiers: methods and algorithms. Wiley, Chichester (2004)MATHCrossRefGoogle Scholar
  9. 9.
    Sandri, S., Dubois, D., Kalfsbeek, H.: Elicitation, assessment and pooling of expert judgments using possibility theory. IEEE Transactions on Fuzzy Systems 3(3), 313–335 (1995)CrossRefGoogle Scholar
  10. 10.
    Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man and Cybernetics 15(4), 580–585 (1985)Google Scholar
  11. 11.
    Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R.: Fuzzy models and algorithms for pattern recognition and image processing. Springer, Heidelberg (1999)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Isabela Drummond
    • 1
  • Sandra Sandri
    • 2
  1. 1.Instituto Nacional de Pesquisas EspaciaisBrasil
  2. 2. BellaterraSpain

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