Abstract
A metaclassifier is a technique that integrates multiple base classifiers. In this paper a hybrid meta-classifier algorithm based on generative and non-generative methods is proposed. Five well-know strong classifiers are used for the non-generative method and bagging was used for generative method. The performances of the five base classifiers, their ensembles based on bagging, and the proposed hybrid metaclassifier are compared using classification error rates. Eight different datasets coming from the UCI Machine Learning database repository are used in the experiments.
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Amin, F., Islam, M., Murase, K.: Ensemble of single-layered complex-valued neural networks for classification tasks. Neurocomputing 72, 2227–2234 (2009)
Blake, C.L., Mertz, C.J., Newman, D.J., Hettich, C.L.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Breiman, L.: Bagging predictors. Machine Learning, Technical Report. Department of Statistics, University of California (1994)
Dietterich, T.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Duin, W., Tax, D.: Experiments with Classifier Combining Rules. In: Pattern Recognition Group, pp. 16–29. University of Technology and Springer, The Netherlands, Heidelberg (2000)
Freund, Y., Schapire, R.: Experiments with a new booosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156. Morgan Kauffman, San Francisco (1996)
Goumas, S.K., Dimou, I.N., Zervakis, M.E.: Combination of multiple classifiers for post-placement quality inspection of components: A comparative study. Information Fusion 11(2), 149–162 (2010)
Hsieh, N.C., Hung, L.P.: A data driven ensemble classifier for credit scoring analysis. Expert Systems with Applications 37(1), 534–545 (2010)
Oza, N.C., Tumer, K.: Classifier ensembles: Select real-world applications. Information Fusion 9(1), 4–20 (2008)
Quinlan, R.: C4.5 Programs for Machine Learning. Published by Morgan Kaufmann Series in Machine Learning (1993)
Roli, F., Giacinto, G., Vernazza, G.: Methods for Designing Multiple Classifier Systems. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 78–87. Springer, Heidelberg (2001)
Tumer, K., Ghosh, J.: Robust Order Statistics-based ensembles for distributed data mining. In: Advances in Distributd and Parallel Knowledge Discovery. AAAI Press/The MIT Press (2000)
Valentini, G., Masulli, F.: Ensembles of Learning Machines. In: Marinaro, M., Tagliaferri, R. (eds.) WIRN 2002. LNCS, vol. 2486, pp. 3–19. Springer, Heidelberg (2002)
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Lozano, E., Acuña, E. (2011). Comparing Classifiers and Metaclassifiers. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2011. Lecture Notes in Computer Science(), vol 6870. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23184-1_5
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DOI: https://doi.org/10.1007/978-3-642-23184-1_5
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