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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References
Akdeniz, E., Egrioglu, E., Bas, E., Yolcu, U.: An ARMA type Pi-Sigma artificial neural network for nonlinear time series forecasting. J. Artif. Intell. Soft Comput. Res. 8(2), 121–132 (2018)
Chatterjee, S., Hadi, A.S.: Regression Analysis by Example. Wiley, Hoboken (2015)
Duarte, J., Gama, J., Bifet, A.: Adaptive model rules from high-speed data streams. ACM Trans. Knowl. Discov. Data (TKDD) 10(3), 30 (2016)
Duda, P., Jaworski, M., Rutkowski, L.: Int. J. Neural Syst. 28, 1750048 [23 pages] (2018). https://doi.org/10.1142/S0129065717500484
Fox, J.: Applied Regression Analysis and Generalized Linear Models. Sage Publications, Thousand Oaks (2015)
Greblicki, W., Pawlak, M.: Nonparametric System Identification, vol. 1. Cambridge University Press, Cambridge (2008)
Ikonomovska, E., Gama, J., Dzeroski, S.: Learning model trees from evolving data streams. Data Min. Knowl. Disc. 23(1), 128–168 (2011)
Ikonomovska, E., et al.: Speeding-up Hoeffding-based regression trees with options. In: Proceedings of 28th International Conference on Machine Learning. Omnipress (2011)
Ikonomovska, E., Gama, J., Dzeroski, S.: Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing 150, 458–470 (2015)
Jaworski, M., Duda, P., Rutkowski, L., Najgebauer, P., Pawlak, M.: Heuristic regression function estimation methods for data streams with concept drift. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10246, pp. 726–737. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59060-8_65
Kadlec, P., Gabrys, B.: Local learning based adaptive soft sensor for catalyst activation prediction. AIChE J. 57(5), 1288–1301 (2011)
Kolter, J.Z., Maloof, M.A.: Using additive expert ensembles to cope with concept drift. In: Proceedings of 22nd International Conference on Machine Learning. ACM (2005)
Krawczyk, B., et al.: Ensemble learning for data stream analysis: a survey. Inf. Fusion 37, 132–156 (2017)
Pietruczuk, L., Rutkowski, L., Jaworski, M., Duda, P.: How to adjust an ensemble size in stream data mining? Inf. Sci. 381, 46–54 (2017)
Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)
Susheela, D.V., Meena, L.: Parallel MCNN (PMCNN) with application to prototype selection on large and streaming data. J. Artif. Intell. Soft Comput. Res. 7(3), 155–169 (2017)
Oza, N.C.: Online bagging and boosting. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2340–2345. IEEE (2005)
Xiao, H., Eckert, C.: Lazy Gaussian process committee for real-time online regression. In: AAAI (2013)
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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