Abstract
Multiclass pattern recognition problems (K > 2) can be decomposed by a tree-structured approach. It constructs an ensemble of K-1 individually trained binary classifiers whose predictions are combined to classify unseen instances. A key factor for an effective ensemble is how to combine its member outputs to give the final decision. Although there are various methods to build the tree structure and to solve the underlying binary problems, there is not much work to develop new combination methods that can best combine these intermediate results. We present here a trainable fusion method that integrates statistical information about the individual outputs (clustered decision templates) into a Radial Basis Function (RBF) network. We compare our model with the decision templates combiner and the existing nontrainable tree ensemble fusion methods: classical decision tree-like approach, product of the unique path and Dempster-Shafer evidence theory based method.
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References
Kumar, S., Ghosh, J., Crawford, M.: A hierarchical multiclassifier system for hyperspectral data analysis. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 270–279. Springer, Heidelberg (2000)
Kumar, S., Ghosh, J., Crawford, M.: Hierarchical fusion of multiple classifiers for hyperspectral data analysis. International Journal of Pattern Analysis and Applications 5(2), 210–220 (2002)
Fay, R., Schwenker, F., Thiel, C., Palm, G.: Hierarchical neural networks utilising Dempster-Shafer evidence theory. In: Schwenker, F., Marinai, S. (eds.) ANNPR 2006. LNCS (LNAI), vol. 4087, pp. 198–209. Springer, Heidelberg (2006)
Fay, R.: Feature selection and information fusion in hierarchical neural networks for iterative 3D-object recognition. Ph.D thesis, Ulm University (2007)
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2(1), 263–286 (1995)
Kuncheva, L., Bezdek, J., Duin, R.: Decision templates for multiple classifier fusion: An experimental comparison. Pattern Recognition 34(2), 299–314 (2001)
Dempster, A.P.: A generalization of bayesian inference. Journal of the Royal Statistical Society, 205–247 (1968)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Wolpert, D.H.: Stacked generalization. Neural Networks 5(6), 1289–1301 (1994)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)
Schwenker, F., Kestler, H., Palm, G.: Three learning phases for radial basis function networks. Neural Networks 14, 439–458 (2001)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Blake, C., Merz, C.: UCI repository of machine learning databases. University of California (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Nadeau, C., Bengio, Y.: Inference for the generalization error. Machine Learning 62, 239–281 (2003)
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Abdel Hady, M.F., Schwenker, F. (2009). Decision Templates Based RBF Network for Tree-Structured Multiple Classifier Fusion. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_10
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DOI: https://doi.org/10.1007/978-3-642-02326-2_10
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