Abstract
This paper proposes an innovative combinational algorithm for improving the performance of classifier ensembles both in stabilities of their results and in their accuracies. The proposed method uses bagging and boosting as the generators of base classifiers. Base classifiers are kept fixed as decision trees during the creation of the ensemble. Then we partition the classifiers using a clustering algorithm. After that by selecting one classifier per each cluster, we produce the final ensemble. The weighted majority vote is taken as consensus function of the ensemble. We evaluate our framework on some real datasets of UCI repository and the results show effectiveness of the algorithm comparing with the original bagging and boosting algorithms.
Chapter PDF
Similar content being viewed by others
References
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Breiman, L.: Bagging Predictors. Journal of Machine Learning 24(2), 123–140 (1996)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Gunter, S., Bunke, H.: Creation of classifier ensembles for handwritten word recognition using feature selection algorithms. IWFHR (2002)
Kuncheva, L.I.: Combining Pattern Classifiers, Methods and Algorithms. Wiley, New York (2005)
Minaei-Bidgoli, B., Topchy, A.P., Punch, W.F.: Ensembles of Partitions via Data Resampling. In: ITCC, pp. 188–192 (2004)
Yang, T.: Computational Verb Decision Trees. International Journal of Computational Cognition, 34–46 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Parvin, H., Minaei-Bidgoli, B., Shahpar, H. (2011). Classifier Selection by Clustering. In: MartÃnez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds) Pattern Recognition. MCPR 2011. Lecture Notes in Computer Science, vol 6718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21587-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-21587-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21586-5
Online ISBN: 978-3-642-21587-2
eBook Packages: Computer ScienceComputer Science (R0)