Abstract
This paper proposes an innovative combinational method how to select the number of clusters in the Classifier Selection by Clustering (CSC) to improve the performance of classifier ensembles both in stabilities of their results and in their accuracies as much as possible. The CSC uses bagging as the generator of base classifiers. Base classifiers are kept fixed as either decision trees or multilayer perceptron during the creation of the ensemble. Then it partitions the classifiers using a clustering algorithm. After that by selecting one classifier per each cluster, it produces the final ensemble. The weighted majority vote is taken as consensus function of the ensemble. Here it is probed how the cluster number affects the performance of the CSC method and how we can switch to a well approximation option for a dataset adaptively. We expand our studies on a large number of real datasets of UCI repository to reach a well conclusion.
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References
Günter, S., Bunke, H.: Creation of Classifier Ensembles for Handwritten Word Recognition Using Feature Selection Algorithms. In: Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR 2002), August 06-08, p. 183 (2002)
Kuncheva, L.I.: Combining Pattern Classifiers, Methods and Algorithms. Wiley, New York (2005)
Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recognition Letters 22, 25–33 (2001)
Breiman, L.: Bagging Predictors. Journal of Machine Learning 24(2), 123–140 (1996)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Yang, T.: Computational Verb Decision Trees. International Journal of Computational Cognition 4(4), 34–46 (2006)
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)
Parvin, H., Minaei-Bidgoli, B., Beigi, A.: A New Classifier Ensembles Framework. Knowledge-Based and Intelligent Information and Engineering Systems, 110–119 (2011)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Minaei-Bidgoli, B., Topchy, A.P., Punch, W.F.: Ensembles of Partitions via Data Resampling. In: ITCC, pp. 188–192
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© 2012 Springer-Verlag Berlin Heidelberg
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Parvin, H., Parvin, S., Rezaei, Z., Mohamadi, M. (2012). Classifier Ensemble Framework Based on Clustering Method. In: Chowdhry, B.S., Shaikh, F.K., Hussain, D.M.A., Uqaili, M.A. (eds) Emerging Trends and Applications in Information Communication Technologies. IMTIC 2012. Communications in Computer and Information Science, vol 281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28962-0_33
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DOI: https://doi.org/10.1007/978-3-642-28962-0_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28961-3
Online ISBN: 978-3-642-28962-0
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