Abstract
We can face with the pattern recognition problems where the influence of hidden context leads to more or less radical changes in the target concept. This paper proposes the mathematical and algorithmic framework for the concept drift in the pattern recognition problems. The probabilistic basis described in this paper is based on the Bayesian approach to the estimation of decision rule parameters. The pattern recognition procedure derived from this approach uses the general principle of the dynamic programming and has linear computational complexity in contrast to polynomial computational complexity in general kind of pattern recognition procedure.
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References
Elwell, R., Polikar, R.: Incremental Learning of Concept Drift in Nonstationary Environments. IEEE Transactions on Neural Networks (2011)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69–101 (1996)
Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: SIAM International Conference on Data Mining (2007)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) KDD, pp. 226–235. ACM Press (2003)
Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: KDD, pp. 377–382. ACM Press (2001)
Bifet, A., Gama, J., Pechenizkiy, M., Zliobaite, I.: Handling Concept Drift: Importance, Challenges and Solutions. In: PAKDD-2011 Tutorial, Shenzhen, China, May 27 (2011)
Vapnik, V.: Statistical Learning Theory. John-Wiley & Sons, Inc. (1998)
Tatarchuk, A.I., Sulimova, V.V., Mottl, V.V., Windridge, D.: Method of relevant potential functions for selective combination of diverse information in the pattern recognition learning based on Bayesian approach. In: MMRO-14: Conf. Proc., Suzdal, pp. 188–191 (2009)
Wiener, N.: Extrapolation, Interpolation, and Smoothing of Stationary Random Time Series with Engineering Applications, 163p. Technology Press of MIT, John Wiley & Sons (1949)
Krasotkina, O.V., Mottl, V.V., Turkov, P.A.: Bayesian Approach to the Pattern Recognition Problem in Nonstationary Environment. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds.) PReMI 2011. LNCS, vol. 6744, pp. 24–29. Springer, Heidelberg (2011)
De Groot, M.: Optimal Statistical Decisions. McGraw-Hill Book Company (1970)
Kostin, A.A., Kopylov, A.V., Mottl, V.V., Muchnik, I.B.: Dynamic Programming Procedures in Nonstationary Signal Analysis. Pattern Recognition and Image Analysis 11(1), 205–208 (2001)
Spambase Data Set, http://www.ics.uci.edu/~mlearn/MLRepository.html
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., Kirkby, R., Gavalda, R.: New ensemble methods for evolving data streams. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: SBIA Brazilian Symposium on Artificial Intelligence, pp. 286–295 (2004)
Bifet, A., Frank, E., Holmes, G., Pfahringer, B.: Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking. Journal of Machine Learning Research Proceedings Track 13, 225–240 (2010)
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Turkov, P., Krasotkina, O., Mottl, V. (2012). Bayesian Approach to the Concept Drift in the Pattern Recognition Problems. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_1
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DOI: https://doi.org/10.1007/978-3-642-31537-4_1
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