Abstract
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.
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References
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)
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)
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)
Khotazad, A., Hong, Y.H.: Invariant image recognition by Zernike Moments. PAMI 12(5), 489–497 (1990)
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)
Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recogn. Lett. 19, 255–259 (1998)
Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recogn. Lett. 18, 689–694 (1997)
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)
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)
Deb, K.: Multi-Objective optimization using Evolutionary algorithms. Wiley, London (2001)
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)
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)
Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithm. Evolutionary Computation 2, 221–248 (1994)
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)
Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary computation 8, 149–172 (2000)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)
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)
Kaufman, L., Rousseeuw, P.J.: Finding groups in data. John Wiley & Sons, Inc., New York (1990)
Kendall, M.G.: Rank Correlation Methods. Hafner Publishing Co, New York (1955)
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)
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Raveaux, R., Eugen, B., Locteau, H., Adam, S., Héroux, P., Trupin, E. (2007). A Graph Classification Approach Using a Multi-objective Genetic Algorithm Application to Symbol Recognition. In: Escolano, F., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2007. Lecture Notes in Computer Science, vol 4538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72903-7_33
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DOI: https://doi.org/10.1007/978-3-540-72903-7_33
Publisher Name: Springer, Berlin, Heidelberg
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