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Pattern Recognition Based on Hierarchical Description of Decision Rules Using Choquet Integral

  • K. C. SantoshEmail author
  • Laurent Wendling
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 709)

Abstract

A hierarchical approach to automatically extract subsets of soft output classifiers, assumed to decision rules, is presented in this paper. Output of classifiers are aggregated into a decision scheme using the Choquet integral. To handle this, two selection schemes are defined, aiming to discard weak or redundant decision rules so that most relevant subsets are restored. For validation, we have used two different datasets: shapes (Sharvit) and graphical symbols (handwritten, CVC - Barcelona). Our experimental study attests the interest of the proposed methods.

Keywords

Choquet integral Selection of decision rules Hierarchical description 

References

  1. 1.
    Bernier, T., Landry, J.A.: A new method for representing and matching shapes of natural objects. Pattern Recogn. 36(8), 1711–1723 (2003)CrossRefGoogle Scholar
  2. 2.
    Andreopoulos, A., Tsotsos, J.K.: 50 years of object recognition: directions forward. Comput. Vis. Image Underst. 117(8), 827–891 (2013)CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)zbMATHGoogle Scholar
  4. 4.
    Choquet, G.: Theory of capacities. Annales de l’Institut Fourier 5, 131–295 (1953)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Cordella, L.P., Vento, M.: Symbol recognition in documents: a col of technics? Int. J. Doc. Anal. Recogn. 3(2), 73–88 (2000)CrossRefGoogle Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2001)zbMATHGoogle Scholar
  7. 7.
    Grabisch, M.: A new algorithm for identifying fuzzy measures and its application to pattern recognition. In: FUZZ’IEEE International Conference, vol. 95, pp. 145–150 (1995)Google Scholar
  8. 8.
    Grabisch, M.: The application of fuzzy integrals in multicriteria decision making. Eur. J. Oper. Res. 89(3), 445–456 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Mauclair, J., Wendling, L., Janiszek, D.: Fuzzy integrals for the aggregation of confidence measures in speech recognition. IEEE FUZZ 2011, 1149–1156 (2011)Google Scholar
  10. 10.
    Trabelsi BenAmeur, S., Cloppet, F., Sellami Masmoudi, D., Wendling, L.: Choquet integral based feature selection for early breast cancer diagnosis from MRIs. ICPRAM 2016, 351–358 (2016)Google Scholar
  11. 11.
    Grabisch, M., Nicolas, J.M.: Classification by fuzzy integral - performance and tests. Fuzzy Sets Syst. 65, 255–271 (1994)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hadjitodorov, S.T., Kuncheva, L.I., Todorova, L.P.: Moderate diversity for better cluster ensembles. Inf. Fusion 7(3), 264–275 (2006)CrossRefGoogle Scholar
  13. 13.
    Ho, T.K.: Multiple classifier combination: lessons and next steps. In: Kandel, A., Bunke, H. (eds.) Hybrid Methods in Pattern Recognition. World Scientific, Singapore (2002)Google Scholar
  14. 14.
    Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)CrossRefGoogle Scholar
  15. 15.
    Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 489–497 (1990)CrossRefGoogle Scholar
  16. 16.
    Kim, W.Y., Kim, Y.-S.: A new region-based shape descriptor. In: TR 15–01, Pisa, Italy (1999)Google Scholar
  17. 17.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)CrossRefGoogle Scholar
  18. 18.
    Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles. Mach. Learn. 51, 181–207 (2003)CrossRefzbMATHGoogle Scholar
  19. 19.
    Britto, A.S., Sabourin, R., Soares de Oliveira, L.E.: Dynamic selection of classifiers - a comprehensive review. Pattern Recogn. 47(11), 3665–3680 (2014)CrossRefGoogle Scholar
  20. 20.
    Langley, P.: Selection of relevant features in machine learning. In: AAAI Fall Symposium on Relevance, pp. 140–144 (1994)Google Scholar
  21. 21.
    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. 104–128. Springer, Heidelberg (2002). doi: 10.1007/3-540-45868-9_9 CrossRefGoogle Scholar
  22. 22.
    Littewood, B., Miller, D.: Conceptual modeling of coincident failures in multiversion software. IEEE Trans. Softw. Eng. 15(12), 1596–1614 (1989)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Marichal, J.L.: Aggregation of interacting criteria by means of the discrete Choquet integral. In: Aggregation Operators: New Trends and Applications, pp. 224–244. Physica-Verlag GmbH, Heidelberg (2002)Google Scholar
  24. 24.
    Matsakis, P., Wendling, L.: A new way to represent the relative position between areal objects. IEEE Trans. Pattern Anal. Mach. Intell. 21(7), 634–643 (1999)CrossRefGoogle Scholar
  25. 25.
    Melnik, O., Vardi, Y., Zhang, C.H.: Mixed group ranks: preference and confidence in classifier combination. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 973–981 (2004)CrossRefGoogle Scholar
  26. 26.
    Mikenina, L., Zimmermann, H.J.: Improved feature selection and classification by the 2-additive fuzzy measure. Fuzzy Sets Syst. 107, 197–218 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Technical report IDSIA-03-14 (2014 )Google Scholar
  28. 28.
    Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: CVPR 2014 (2014)Google Scholar
  29. 29.
    Murofushi, T., Soneda, S.: Techniques for reading fuzzy measures (iii): interaction index. In: Proceedings of the 9th Fuzzy Set System, pp. 693–696 (1993)Google Scholar
  30. 30.
    Murofushi, T., Sugeno, M.: A theory of fuzzy measures: representations, the Choquet integral, and null sets. J. Math. Anal. Appl. 159, 532–549 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Rendek, J., Wendling, L.: On determining suitable subsets of decision rules using Choquet integral. Int. J. Pattern Recogn. Artif. Intell. 22(2), 207–232 (2008)CrossRefGoogle Scholar
  32. 32.
    Ruta, D., Gabrys, B.: An overview of classifier fusion methods. Comput. Inf. Syst. 7, 1–10 (2000)Google Scholar
  33. 33.
    Schapire, R.E., Fruend, Y., Bartlett, P., Lee, W.: Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Stat. 26(5), 1651–1689 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Schmitt, E., Bombardier, V., Wendling, L.: Improving fuzzy rule classifier by extracting suitable features from capacities with respect to Choquet integral. IEEE Trans. Syst. Man Cybern. Part B 38(5), 1195–1206 (2008)CrossRefGoogle Scholar
  35. 35.
    Shapley, L.: A value for n-person games. In: Khun, H., Tucker, A. (eds.) Ann. Math. Stud., pp. 307–317. Princeton University Press, Princeton (1953)Google Scholar
  36. 36.
    Sharvit, D., Chan, J., Tek, H., Kimia, B.: Symmetry-based indexing of image databases. J. Vis. Commun. Image Represent. 12, 366–380 (1998)CrossRefGoogle Scholar
  37. 37.
    Stejic, Z., Takama, Y., Hirota, K.: Mathematical aggregation operators in image retrieval: effect on retrieval performance and role in relevance feedback. Sig. Process. 85(2), 297–324 (2005)CrossRefzbMATHGoogle Scholar
  38. 38.
    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). doi: 10.1007/978-3-540-39966-7_17 CrossRefGoogle Scholar
  39. 39.
    Teague, R.: Image analysis via the general theory of moments. J. Opt. Soc. Am. 70(8), 920–930 (1979)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Yager, R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)CrossRefzbMATHGoogle Scholar
  41. 41.
    Yang, S.: Symbol recognition via statistical integration of pixel-level constraint histograms: a new descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 278–281 (2005)CrossRefGoogle Scholar
  42. 42.
    Zhang, D., Lu, G.: Shape-based image retrieval using generic Fourier descriptor. Sig. Process. Image Commun. 17, 825–848 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of South DakotaVermillionUSA
  2. 2.LIPADE - Université Paris Descartes (Paris V)Paris Cedex 06France

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