Abstract
In this paper, a novel procedure for regression analysis in the case of non-stationary data streams is presented. Despite numerous applications, the regression task is rarely considered in a scientific literature, e.g. compared to classification task. The proposed method applies an ensemble technique to deal with data streams (especially with concept drift). As weak learners, a nonparametric estimator of regression is used. Every single weak model (weak learner) is able to track a specific type of the non-stationarity. The experimental section demonstrates that the proposed algorithm allows for tracking different types nonstationarities and increases accuracy with respect to a single estimator.
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Acknowledgments
This work was supported by the Polish National Science Centre under Grant No. 2014/15/B/ST7/05264.
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Duda, P., Jaworski, M., Rutkowski, L. (2018). Online GRNN-Based Ensembles for Regression on Evolving Data Streams. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_26
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DOI: https://doi.org/10.1007/978-3-319-92537-0_26
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