Abstract
This work addresses the problem of mining data stream generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnosis degradations of this process, using change detection mechanisms, and self-repairs the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learns can detect re-occurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dijkstra, W.: Self-stabilizing systems in spite of distributed control. Communications of the ACM 17(11), 643–644 (1974)
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)
Granitzer, M., Kröll, M., Seifert, C., Rath, A.S., Weber, N., Dietzel, O., Lindstaedt, S.N.: Analysis of machine learning techniques for context extraction. In: Pichappan, P., Abraham, A. (eds.) ICDIM, pp. 233–240. IEEE, Los Alamitos (2008)
Grant, E., Leavenworth, R.: Statistical Quality Control. McGraw-Hill, New York (1996)
Harries, M.B., Sammut, C., Horn, K.: Extracting hidden context. Machine Learning 32, 101–126 (1998)
Katakis, I., Tsoumakas, G., Vlahavas, I.: Tracking recurring contexts using ensemble classifiers: an application to email filtering. Knowledge and Information Systems 22, 371–391 (2010)
Klinkenberg, R.: Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis 8(3), 281–300 (2004)
Lazarescu, M.M.: A multi-resolution learning approach to tracking concept drift and recurrent concepts. In: Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (2005)
Ortega, J.: Exploiting multiple existing models and learning algorithms. In: AAAI 1996 - Workshop in Induction of Multiple Learning Models, pp. 17–21 (1995)
Ortega, J., Koppel, M., Argamon, S.: Arbitrating among competing classifiers using learned referees. Knowledge and Information Systems 3(4), 470–490 (2001)
Ramamurthy, S., Bhatnagar, R.: Tracking recurrent concept drift in streaming data using ensemble classifiers. In: ICMLA 2007: Proceedings of the Sixth International Conference on Machine Learning and Applications, pp. 404–409. IEEE Computer Society, Washington, DC, USA (2007)
Seewald, A., Fürnkranz, J.: An evaluation of grading classifiers. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds.) IDA 2001. LNCS, vol. 2189, pp. 115–124. Springer, Heidelberg (2001)
Nick Street, W., Kim, Y.: A streaming ensemble algorithm (sea) for large-scale classification. In: Knowledge Discovery and Data Mining, pp. 377–382. ACM Press, New York (2001)
Turney, P.: The management of context-sensitive features: A review of strategies (1996)
Widmer, G.: Tracking context changes through meta-learning. Machine Learning 27(3), 259–286 (1997)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)
Yang, Y., Wu, X., Zhu, X.: Mining in anticipation for concept change: Proactive-reactive prediction in data streams. Data Mining and Knowledge Discovery 13(3), 261–289 (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
Gama, J., Kosina, P. (2011). Learning about the Learning Process. In: Gama, J., Bradley, E., Hollmén, J. (eds) Advances in Intelligent Data Analysis X. IDA 2011. Lecture Notes in Computer Science, vol 7014. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24800-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-24800-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24799-6
Online ISBN: 978-3-642-24800-9
eBook Packages: Computer ScienceComputer Science (R0)