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Selection of Suitable Set of Decision Rules Using Choquet Integral

  • Laurent Wendling
  • Jan Rendek
  • Pascal Matsakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)

Abstract

An approach to automatically extract pertinent subsets of soft output classifiers, assumed to decision rules, is presented in this paper. They are aggregated into a global decision scheme using the Choquet integral. A selection scheme is defined that discards weak or redundant decision rules, keeping only the most relevant subset. An experimental study, based on real world data attest the interest of our method.

Keywords

Decision Rule Recognition Rate Aggregation Operator Fuzzy Measure Interaction 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.

References

  1. 1.
    Bernier, T., Landry, J.-A.: A new method for representing and matching shapes of natural objects. Pattern Recognition 36(8), 1711–1723 (2003)CrossRefGoogle Scholar
  2. 2.
    Choquet, G.: Theory of capacities. Annales de l’Institut Fourier 5, 131–295 (1953)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Cordella, L.P., Vento, M.: Symbol recognition in documents: a col of technics? International Journal of Document Analysis and Recognition 3(2), 73–88 (2000)CrossRefGoogle Scholar
  4. 4.
    Grabisch, M.: A new algorithm for identifying fuzzy measures and its application to pattern recognition. In: Int. conf. FUZZ’IEEE 1995, pp. 145–150 (1995)Google Scholar
  5. 5.
    Grabisch, M., Nicolas, J.M.: Classification by fuzzy integral - performance and tests. Fuzzy Sets and Systems 65, 255–271 (1994)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Jain, A.K., Duin, R.P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  7. 7.
    Khotanzad, A., Hong, Y.H.: Invariant Image Recognition by Zernike. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 489–497 (1990)CrossRefGoogle Scholar
  8. 8.
    Kim, W.-Y., Kim, Y.-S.: A new region-based shape descriptor. In: TR 15-01, Pisa, Italy (1999)Google Scholar
  9. 9.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  10. 10.
    Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles. Machine Learning 51, 181–207 (2003)CrossRefzbMATHGoogle Scholar
  11. 11.
    Lladós, J., Valveny, E., Sánchez, G., Martí, E.: Symbol Recognition: Current Advances and Perspectives. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 104–127. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Matsakis, P., Wendling, L.: A New Way to Represent the Relative Position Between Areal Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(7), 634–643 (1999)CrossRefGoogle Scholar
  13. 13.
    Mikenina, L., Zimmermann, H.-J.: Improved feature selection and classification by the 2-additive fuzzy measure. Fuzzy Sets and Systems 107, 197–218 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Murofushi, T., Soneda, S.: Techniques for reading fuzzy measures(iii): interaction index. In: Proc. of the 9th Fuzzy Set System, pp. 693–696 (1993)Google Scholar
  15. 15.
    Murofushi, T., Sugeno, M.: A theory of fuzzy measures: representations, the Choquet integral, and null sets. Journal of Math. Anal. Appl. 159, 532–549 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Ruta, D., Gabrys, B.: An overview of classifier fusion methods. Comp. and Information System 7(1–10) (2000)Google Scholar
  17. 17.
    Shapley, L.: A value for n-person games. In: Khun, H., Tucker, A. (eds.) Annals of Mathematics Studies, pp. 307–317. Princeton University Press, Princeton (1953)Google Scholar
  18. 18.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: CB Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  19. 19.
    Stejic, Z., Takama, Y., Hirota, K.: Mathematical aggregation operators in image retrieval: effect on retrieval performance and role in relevance feedback. Signal Processing 85(2), 297–324 (2005)CrossRefzbMATHGoogle Scholar
  20. 20.
    Tabbone, S., Wendling, L.: Binary shape normalization using the Radon transform. In: Nyström, I., Sanniti di Baja, G., Svensson, S. (eds.) DGCI 2003. LNCS, vol. 2886, pp. 184–193. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  21. 21.
    Yang, S.: Symbol Recognition via Statistical Integration of Pixel-Level Constraint Histograms: A New Descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(2), 278–281 (2005)CrossRefGoogle Scholar
  22. 22.
    Zhang, D., Lu, G.: Shape-based image retrieval using generic fourier descriptor. Signal Processing: Image Communication 17, 825–848 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Laurent Wendling
    • 1
  • Jan Rendek
    • 1
  • Pascal Matsakis
    • 2
  1. 1.LORIA - ESIALUniversité Henri PoincaréVandœuvre-lés-NancyFrance
  2. 2.CIS DptUniversité de GuelphCanada

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