Abstract
This paper presents a new framework for dealing with two main types of concept drift: sudden and gradual drift in labelled data with decision attribute. The learning examples are processed in batches of the same size. This new framework, called Batch Weighted Ensemble, is based on incorporating drift detector into the evolving ensemble. Its performance was evaluated experimentaly on data sets with different types of concept drift and compared with the performance of a standard Accuracy Weighted Ensemble classifier. The results show that BWE improves evaluation measures like processing time, memory used and obtain competitive total accuracy.
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References
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: Massive Online Analysis. Journal of Machine Learning Research, JMLR (2010), http://sourceforge.net/projects/moa-datastream/
Bifet, A., Holmes, G., Pfahringer, B., Kranen, P., Kremer, H., Jansen, T., Seidl, T.: MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. In: Workshop on Applications of Pattern Analysis, HaCDAIS (2010)
Brzezinski, D.: Mining data streams with concept drift. Master’s thesis supervised by J.Stefanowski, Poznan University of Technology, Poznań, Poland (2010)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with Drift Detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004)
Gama, J.: Knowledge Discovery from Data Streams. CRC (2010)
Kuncheva, L.I.: Classifier Ensembles for Changing Environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004)
Kuncheva, L.I.: Classifier ensembles for detecting concept change in streaming data: Overview and perspectives. In: Proceedings 2nd Workshop SUEMA 2008 (ECAI 2008), Greece, pp. 5–10 (2008)
Schlimmer, J., Granger, R.: Beyond Incremental Processing: Tracking Concept Drift. AAAI, Menlo Park (1986)
Tsymbal, A.: The problem of concept drift: Definitions and related work. Technical Report, Trinity College, Dublin, Ireland (2004)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings ACM SIGKDD, pp. 226–235 (2003)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69–101 (1996)
Zliobaite, I.: Learning under Concept Drift: an Overview. Technical Report, Vilnius University, Lithuania (2009)
Zhang, P., Zhu, X., Guo, L.: Mining Data Streams with Labeled and Unlabeled Training Examples. In: Perner, P. (ed.) ICDM 2009. LNCS, vol. 5633, pp. 627–636. Springer, Heidelberg (2009)
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Deckert, M. (2011). Batch Weighted Ensemble for Mining Data Streams with Concept Drift. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_32
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DOI: https://doi.org/10.1007/978-3-642-21916-0_32
Publisher Name: Springer, Berlin, Heidelberg
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