Advertisement

Multiple Classifiers for Graph of Words Embedding

  • Jaume Gibert
  • Ernest Valveny
  • Oriol Ramos Terrades
  • Horst Bunke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)

Abstract

During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs. The graph of words embedding assigns a feature vector to every graph by counting unary and binary relations between node representatives and combining these pieces of information into a single vector. Selecting different node representatives leads to different vectorial representations and therefore to different base classifiers that can be combined. We experimentally show how this methodology significantly improves the classification of graphs with respect to single base classifiers.

Keywords

Feature Vector Node Attribute Vocabulary Size Feature Subset Selection Borda Count 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. John Wiley, Chichester (2004)CrossRefzbMATHGoogle Scholar
  2. 2.
    Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)CrossRefGoogle Scholar
  3. 3.
    Schenker, A., Bunke, H., Last, M., Kandel, A.: Building Graph-Based Classifier Ensembles by Random Node Selection. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 214–222. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Lee, W.J., Duin, R.P.W.: A Labelled Graph Based Multiple Classifier System. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 201–210. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Lee, W.J., Duin, R.P.W., Bunke, H.: Selecting Structural Base Classifiers for Graph-based Multiple Classifier Systems. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 155–164. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Riesen, K., Bunke, H.: Classifier Ensembles for Vector Space Embedding of Graphs. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 220–230. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Luo, B., Wilson, R.C., Hancock, E.R.: Spectral embedding of graphs. Pattern Recognition 36(10), 2213–2230 (2003)CrossRefzbMATHGoogle Scholar
  8. 8.
    Robles-Kelly, A., Hancock, E.R.: A Riemannian approach to graph embedding. Pattern Recognition 40(3), 1042–1056 (2007)CrossRefzbMATHGoogle Scholar
  9. 9.
    Gibert, J., Valveny, E., Bunke, H.: Graph of Words Embedding for Molecular Structure-Activity Relationship Analysis. In: Bloch, I., Cesar Jr., R.M. (eds.) CIARP 2010. LNCS, vol. 6419, pp. 30–37. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Dance, C., Willamowski, J., Fan, L., Bray, C., Csurka, G.: Visual categorization with bags of keypoints. In: ECCV International Workshop on Statistical Learning in Computer Vision, pp. 1–22 (2004)Google Scholar
  11. 11.
    Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar
  12. 12.
    Gibert, J., Valveny, E., Bunke, H.: Dimensionality Reduction for Graph of Words Embedding. In: Jiang, X. (ed.) GbRPR 2011. LNCS, vol. 6658, pp. 22–31. Springer, Heidelberg (2011)Google Scholar
  13. 13.
    Xu, L., Krzyzak, A., Suen, C.Y.: Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition. IEEE Trans. on Systems, Man and Cybernetics 22(3), 418–425 (1992)CrossRefGoogle Scholar
  14. 14.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  15. 15.
    Ramos Terrades, O., Valveny, E., Tabbone, S.: Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework. IEEE Trans. on Pattern Analysis and Machine Intelligence 31, 1630–1644 (2009)CrossRefGoogle Scholar
  16. 16.
    Riesen, K., Bunke, H.: IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) S+SSPR 2008. LNCS, vol. 5342, pp. 287–297. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Watson, C., Wilson, C.: NIST Special Database 4, Fingerprint Database. National Institute of Standards and Technology (1992)Google Scholar
  18. 18.
    Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)Google Scholar
  19. 19.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for Support Vector Machines. Software (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jaume Gibert
    • 1
  • Ernest Valveny
    • 1
  • Oriol Ramos Terrades
    • 1
  • Horst Bunke
    • 2
  1. 1.Computer Vision CenterUniversitat Autònoma de BarcelonaBellaterraSpain
  2. 2.Institute for Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

Personalised recommendations