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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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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