Abstract
Classifier ensembles aim at more accurate classifications than single classifiers. In the present paper we introduce a general approach to building structural classifier ensembles, i.e. classifiers that make use of graphs as representation formalism. The proposed methodology is based on a recent graph edit distance approximation. The major observation that motivates the use of this particular approximation is that the resulting distances crucially depend on the order of the nodes of the underlying graphs. Our novel methodology randomly permutes the node order \(N\) times such that the procedure leads to \(N\) different distance approximations. Next, a distance based classifier is trained for each approximation and the results of the individual classifiers are combined in an appropriate way. In several experimental evaluations we make investigations on the classification accuracy of the resulting classifier ensemble and compare it with two single classifier systems.
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Notes
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For the MAO data only a small training set is available and thus we conduct a leave-one out experiment on this data set.
References
Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New Jersey (2004)
Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2008)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)
Freund, Y., Shapire, R.: A decision theoretic generalization of online learning and application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)
Shapire, R., Freund, Y., Bartlett, P., Lee, W.: Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Stat. 26(5), 1651–1686 (1998)
Ho, T.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Cook, D., Holder, L. (eds.): Mining Graph Data. Wiley-Interscience, New York (2007)
Gärtner, T., Horvath, T., Wrobel, S.: Graph kernels. Encycl. Mach. Learn. 2010, 467–469 (2010)
Marcialis, G., Roli, F., Serrau, A.: Fusion of statistical and structural fingerprint classifiers. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 310–317. Springer, Heidelberg (2003)
Neuhaus, M., Bunke, H.: Graph-based multiple classifier systems a data level fusion approach. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 479–486. Springer, Heidelberg (2005)
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)
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)
Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. J. Pattern Recogn. Art Intelligence 18(3), 265–298 (2004)
Foggia, P., Percannella, G.: Graph matching and learning in pattern recognition in the last 10 years. Int. J. Pattern Recogn. Art Intell. 28(1), 40 (2014)
Sanfeliu, A., Fu, K.: A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. Syst. Man Cybern. (Part B) 13(3), 353–363 (1983)
Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recogn. Lett. 1, 245–253 (1983)
Riesen, K., Ferrer, M., Dornberger, R., Bunke, H.: Greedy graph edit distance (2015). Submitted to MLDM
Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27(4), 950–959 (2009)
Burkard, R., Dell’Amico, M., Martello, S.: Assignment Problems. Society for Industrial and Applied Mathematics, Philadelphia (2009)
Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Indus. Appl. Math. 5(1), 32–38 (1957)
Riesen, K., Ferrer, M., Fischer, A., Bunke, H.: Approximation of graph edit distance in quadratic time (2015). Submitted to GbR
Riesen, K., Bunke, H.: Graph classification based on vector space embedding. Int. J. Pattern Recogn. Artif. Intell. 23(6), 1053–1081 (2008)
Neuhaus, M., Bunke, H.: Bridging the Gap Between Graph Edit Distance and Kernel Machines. World Scientific, Switzerland (2007)
Riesen, K., Bunke, H.: IAM graph database repository for graph based pattern recognition and machine learning. In: da Vitoria, L., et al. (eds.) Structural, Syntactic, and Statistical Pattern Recognition. LNCS, vol. 5342, pp. 287–297. Springer, Heidelberg (2008)
Acknowledgements
This work has been supported by the Swiss National Science Foundation (SNSF) projects Nr. 200021_153249 and P300P2_1512 as well as the Hasler Foundation Switzerland.
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Riesen, K., Ferrer, M., Fischer, A. (2015). Building Classifier Ensembles Using Greedy Graph Edit Distance. In: Schwenker, F., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2015. Lecture Notes in Computer Science(), vol 9132. Springer, Cham. https://doi.org/10.1007/978-3-319-20248-8_11
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DOI: https://doi.org/10.1007/978-3-319-20248-8_11
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