Abstract
The extension of machine learning methods from static to dynamic environments has received increasing attention in recent years; in particular, a large number of algorithms for learning from so-called data streams has been developed. An important property of dynamic environments is non-stationarity, i.e., the assumption of an underlying data generating process that may change over time. Correspondingly, the ability to properly react to so-called concept change is considered as an important feature of learning algorithms. In this paper, we propose a new type of experimental analysis, called recovery analysis, which is aimed at assessing the ability of a learner to discover a concept change quickly, and to take appropriate measures to maintain the quality and generalization performance of the model.
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References
Ben-David, S., Gehrke, J., Kifer, D.: Detecting change in data streams. In: Proc. VLDB 2004 (2004)
Bifet, A., Kirkby, R.: Massive Online Analysis Manual (August 2009)
Domingos, P., Hulten, G.: A general framework for mining massive data streams. Journal of Computational and Graphical Statistics 12 (2003)
Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proc. KDD 2000, pp. 71–80 (2000)
Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Transactions on Neural Networks 22(10), 1517–1531 (2011)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: A review. ACM SIGMOD Record 34(1) (2005)
Gama, J.: A survey on learning from data streams: current and future trends. Progress in Artificial Intelligence 1(1), 45–55 (2012)
Gama, J.: Knowledge Discovery from Data Streams. Chapman & Hall/CRC (2010)
Gama, J., Gaber, M.M.: Learning from Data Streams. Springer (2007)
Lughofer, E.: FLEXFIS: A robust incremental learning approach for evolving Takagi-Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems 16(6), 1393–1410 (2008)
Shaker, A., Hüllermeier, E.: IBLStreams: A system for instance-based classification and regression on data streams. Evolving Systems 3(4), 235–249 (2012)
Shaker, A., Senge, R., Hüllermeier, E.: Evolving fuzzy pattern trees for binary classification on data streams. Information Sciences 220, 34–45 (2013)
Žliobaite, I., Pechenizkiy, M.: Reference framework for handling concept drift: An application perspective. Technical report (2010)
Widmer, G., Kubat, M.: Effective learning in dynamic environments by explicit context tracking. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 227–243. Springer, Heidelberg (1993)
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Shaker, A., Hüllermeier, E. (2013). Recovery Analysis for Adaptive Learning from Non-stationary Data Streams. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_28
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DOI: https://doi.org/10.1007/978-3-319-00969-8_28
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00968-1
Online ISBN: 978-3-319-00969-8
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