Abstract
The paper deals with the problem of data stream classification. In the previous works we proposed the WAE (Weighted Aging Ensemble) algorithm which may change the line-up of the classifier committee dynamically according to coming of new individual classifiers. The ensemble pruning method uses the diversity measure called the Generalized Diversity only. In this work we propose the modification of the WAE algorithm which applies the mentioned above pruning criterion by the linear combination of diversity measure and accuracy of the classifier ensemble. The proposed method was evaluated on the basis of computer experiments which were carried out on two benchmark databases. The main objective of the experiments was to answer the question if the chosen modified criterion based on the diversity measure and accuracy is an appropriate choice to prune the classifier ensemble dedicated to data stream classification task.
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Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)
Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proc. of the 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 97–106 (2001)
Klinkenberg, R., Renz, I.: Adaptive information filtering: Learning in the presence of concept drifts, pp. 33–40 (1998)
Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: a new ensemble method for tracking concept drift. In: Third IEEE International Conference on Data Mining, pp. 123–130 (November 2003)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)
Kuncheva, L.I.: Classifier ensembles for detecting concept change in streaming data: Overview and perspectives. In: 2nd Workshop SUEMA 2008 (ECAI 2008), pp. 5–10 (2008)
Le Cessie, S., Van Houwelingen, J.C.: Ridge estimators in logistic regression. Applied Statistics, 191–201 (1992)
Narasimhamurthy, A., Kuncheva, L.I.: A framework for generating data to simulate changing environments. In: Proceedings of the 25th IASTED International Multi-Conference: Artificial Intelligence and Applications, AIAP 2007, Anaheim, CA, USA, pp. 384–389. ACTA Press (2007)
Partridge, D., Krzanowski, W.: Software diversity: practical statistics for its measurement and exploitation. Information and Software Technology 39(10), 707–717 (1997)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods, pp. 185–208. MIT Press, Cambridge (1999)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)
Street, W.N., Kim, Y.: A streaming ensemble algorithm (sea) for large-scale classification. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2001, pp. 377–382. ACM, New York (2001)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 226–235. ACM, New York (2003)
Woźniak, M., Kasprzak, A., Cal, P.: Weighted aging classifier ensemble for the incremental drifted data streams. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds.) FQAS 2013. LNCS, vol. 8132, pp. 579–588. Springer, Heidelberg (2013)
Xu, X.: Stream data mining repository (2010), http://www.cse.fau.edu/~xqzhu/stream.html
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Woźniak, M., Cal, P., Cyganek, B. (2014). The Influence of a Classifiers’ Diversity on the Quality of Weighted Aging Ensemble. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_10
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DOI: https://doi.org/10.1007/978-3-319-05458-2_10
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