A Graph Classification Approach Using a Multi-objective Genetic Algorithm Application to Symbol Recognition

  • Romain Raveaux
  • Barbu Eugen
  • Hervé Locteau
  • Sébastien Adam
  • Pierre Héroux
  • Eric Trupin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4538)


In this paper, a graph classification approach based on a multi-objective genetic algorithm is presented. The method consists in the learning of sets composed of synthetic graph prototypes which are used for a classification step. These learning graphs are generated by simultaneously maximizing the recognition rate while minimizing the confusion rate. Using such an approach the algorithm provides a range of solutions, the couples (confusion, recognition) which suit to the needs of the system. Experiments are performed on real data sets, representing 10 symbols. These tests demonstrate the interest to produce prototypes instead of finding representatives which simply belong to the data set.


graph classification multi-objective optimization machine learning graph dissimilarity measure 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Schenker, A., Last, M., Bunke, H., Kandel, A.: Classification of web documents using a graph model. In: Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR), pp. 240–244 ( 2003) Google Scholar
  2. 2.
    King, R.D., Sternberg, M.J.E., Srinivasan, Muggleton, S.H.: Knowledge discovery in a database mutagenetic chemicals. In: proceedings of the workshop Statistics, machine leaning, discovery in databases at the ECML 1995 ( 1995) Google Scholar
  3. 3.
    Cordela, L.P., Vento, M.: Symbol recognition in documents: a collection of techniques? International Journal on Document Analysis and Recognition 3(2), 73–88 (2000)Google Scholar
  4. 4.
    Khotazad, A., Hong, Y.H.: Invariant image recognition by Zernike Moments. PAMI 12(5), 489–497 (1990)Google Scholar
  5. 5.
    Valveny, E., Dosch, P.: Symbol Recognition Contest: A Synthesis. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 368–385. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recogn. Lett. 19, 255–259 (1998)MATHCrossRefGoogle Scholar
  7. 7.
    Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recogn. Lett. 18, 689–694 (1997)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Kriegel, H.P., Schönauer, S.: Similarity Search in Structured Data. In: Kambayashi, Y., Mohania, M.K., Wöß, W. (eds.) Data Warehousing and Knowledge Discovery. LNCS, vol. 2737, pp. 309–319. Springer, Heidelberg (2003)Google Scholar
  9. 9.
    Lopresti, D.P., Wilfong, G.T.: A fast technique for comparing graph representations with applications to performance evaluation. International Journal on Document Analysis and Recognition 6, 219–229 (2003)CrossRefGoogle Scholar
  10. 10.
    Deb, K.: Multi-Objective optimization using Evolutionary algorithms. Wiley, London (2001)MATHGoogle Scholar
  11. 11.
    Schaffer, J.D., Grefenstette, J.J.: Multiobjective learning via genetic algorithms. In: Proceedings of the 9th international joint conference on artificial intelligence, Los Angeles, California, pp. 593-595 (1985) Google Scholar
  12. 12.
    Fonseca, C.M., Fleming, P.J.: Genetic algorithm for multi-objective optimization: formulation, discussion and generalization. In: Stephanie editor, Proceedings of the fifth international conference on genetic algorithm, San Mateo, California, pp. 416–423 (1993) Google Scholar
  13. 13.
    Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithm. Evolutionary Computation 2, 221–248 (1994)CrossRefGoogle Scholar
  14. 14.
    Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2000)CrossRefGoogle Scholar
  15. 15.
    Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary computation 8, 149–172 (2000)CrossRefGoogle Scholar
  16. 16.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)CrossRefGoogle Scholar
  17. 17.
    Coello, C.A.: A short tutorial on Evolutionary Multiobjective Optimisation. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 21–40. Springer, Heidelberg (2001)Google Scholar
  18. 18.
    Kaufman, L., Rousseeuw, P.J.: Finding groups in data. John Wiley & Sons, Inc., New York (1990)Google Scholar
  19. 19.
    Kendall, M.G.: Rank Correlation Methods. Hafner Publishing Co, New York (1955)MATHGoogle Scholar
  20. 20.
    Sorlin, S., Solnon, C.: Reactive Tabu Search for Measuring Graph Similarity. In: Brun, L., Vento, M. (eds.) GbRPR 2005. LNCS, vol. 3434, pp. 172–182. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Romain Raveaux
    • 1
  • Barbu Eugen
    • 2
  • Hervé Locteau
    • 2
  • Sébastien Adam
    • 2
  • Pierre Héroux
    • 2
  • Eric Trupin
    • 2
  1. 1.L3I Laboratory – University of La RochelleFrance
  2. 2.LITIS Labs – University of RouenFrance

Personalised recommendations