Abstract
In this paper we introduce a method of creating structural (i.e. graph-based) classifier ensembles through random node selection. Different k-Nearest Neighbor classifiers, based on a graph distance measure, are created automatically by randomly removing nodes in each prototype graph, similar to random feature subset selection for creating ensembles of statistical classifiers. These classifiers are then combined using a Borda ranking scheme to form a multiple classifier system. We examine the performance of this method when classifying a web document collection; experimental results show the proposed method can outperform a single classifier approach (using either a graph-based or vector-based representation).
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References
Dumais, S., Chen, H.: Hierarchical classification of web content. In: Proceedings of SIGIR 2000, 23rd ACM International Conference on Research and Development in Information Retrieval, pp. 256–263 (2000)
McCallum, A., Nigam, K.: A comparison of event models for naive Bayes text classification. In: Proceedings of the AAAI 1998 Workshop on Learning for Text Categorization (1998)
Apte, C., Damerau, F., Weiss, S.M.: Automated learning of decision rules for text categorization. ACM Transactions on Information Systems 12, 233–251 (1994)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, p. 1. Springer, Heidelberg (2000)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 832–844 (1998)
Salton, G.: Automatic Text Processing: the Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading (1989)
Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters 19, 225–259 (1998)
Fernández, M.L., Valiente, G.: A graph distance metric combining maximum common subgraph and minimum common supergraph. Pattern Recognition Letters 22, 753–758 (2001)
Wallis, W.D., Shoubridge, P., Kraetz, M., Ray, D.: Graph distances using graph union. Pattern Recognition Letters 22, 701–704 (2001)
Schenker, A., Last, M., Bunke, H., Kandel, A.: Classification of web documents using graph matching. International Journal of Pattern Recognition and Artificial Intelligence (to appear)
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 2003), pp. 240–244 (2003)
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)
Tan, C.M., Wang, Y.F., Lee, C.D.: The use of bigrams to enhance text categorization. Information Processing and Management 38, 529–546 (2002)
Mitchell, T.M.: Machine Learning. McGraw-Hill, Boston (1997)
Ho, T.K., Hull, J.J., Srihari, S.N.: On multiple classifier systems for pattern recognition. In: Proceedings of the 11th International Conference on Pattern Recognition, The Hague, The Netherlands, pp. 84–87 (1992)
Lam, L., Huang, Y.S., Suen, C.: Combination of multiple classifier decisions for optical character recognition. In: Bunke, H., Wang, P. (eds.) Handbook of Character Recognition and Document Image Analysis, pp. 79–101. World Scientific, Singapore (1997)
Schenker, A., Last, M., Bunke, H., Kandel, A.: Graph representations for web document clustering. In: Proceedings of the 1st Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 935–942 (2003)
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Schenker, A., Bunke, H., Last, M., Kandel, A. (2004). Building Graph-Based Classifier Ensembles by Random Node Selection. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_21
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DOI: https://doi.org/10.1007/978-3-540-25966-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22144-9
Online ISBN: 978-3-540-25966-4
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