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2018 | OriginalPaper | Chapter

On Ensemble Components Selection in Data Streams Scenario with Gradual Concept-Drift

Author : Piotr Duda

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

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Abstract

In the paper we study the issue of components selection of an ensemble for data stream classification. Decision about adding or removing single component has significant meaning not only for an accuracy in the current instant, but can be also significant for the further stream processing. The algorithm proposed in this paper is an enhanced version of the ASE (Automatically Sized Ensemble) algorithm which guarantees that a new component will be added to the ensemble only if it increases the accuracy not only for the current data chunk but also for the whole data stream. The algorithm is designed to improve data stream processing in the case when one concept is gradually replaced by the other. The Hellinger distance is applied to allow adding a new component, if its predictions differ significantly from the rest of the ensemble, even though that component does not increase accuracy of the whole ensemble.

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Amini, A., Wah, T.Y., Saboohi, H.: On density-based data streams clustering algorithms: a survey. J. Comput. Sci. Technol. 29(1), 116–141 (2014)CrossRef

Andressian, V., Parent, E., Claude, M.: A distributions free test to detect gradual changes in watershed behavior. Water Resour. Res. 39(9) (2003). https://doi.org/10.1029/2003WR002081

Ayadi, N., Derbel, N., Morette, N., Novales, C., Poisson, G.: Simulation and experimental evaluation of the ekf simultaneous localization and mapping algorithm on the wifibot mobile robot. J. Artif. Intell. Soft Comput. Res. 8(2), 91–101 (2018). https://doi.org/10.1515/jaiscr-2018-0006CrossRef

Beygelzimer, A., Kale, S., Luo, H.: Optimal and adaptive algorithms for online boosting. In: Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pp. 2323–2331 (2015)

Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11(May), 1601–1604 (2010)

Bustamam, A., Sarwinda, D., Ardenaswari, G.: Texture and gene expression analysis of the MRI brain in detection of Alzheimers disease. J. Artif. Intell. Soft Comput. Res. 8(2), 111–120 (2018). https://doi.org/10.1515/jaiscr-2018-0008CrossRef

Cao, Y., He, H., Man, H.: SOMKE: Kernel density estimation over data streams by sequences of self-organizing maps. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1254–1268 (2012)CrossRef

Davis, J.J.J., Lin, C.T., Gillett, G., Kozma, R.: An integrative approach to analyze EEG signals and human brain dynamics in different cognitive states. J. Artif. Intell. Soft Comput. Res. 7(4), 287–299 (2017)CrossRef

Devi, V.S., 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)CrossRef

10.

Ditzler, G., Polikar, R.: Incremental learning of concept drift from streaming imbalanced data. IEEE Trans. Knowl. Data Eng. 25(10), 2283–2301 (2013)CrossRef

11.

Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)

12.

Duda, P., Jaworski, M., Rutkowski, L.: Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks. Inf. Sci. (2017). https://doi.org/10.1016/j.ins.2017.07.013MathSciNetCrossRef

13.

Duda, P., Jaworski, M., Rutkowski, L.: Convergent time-varying regression models for data streams: tracking concept drift by the recursive parzen-based generalized regression neural networks. Int. J. Neural Syst. 28(02), 1750048 (2018)CrossRef

14.

Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517–1531 (2011)CrossRef

15.

Hoffmann, M., Vetter, M., Dette, H.: Nonparametric inference of gradual changes in the jump behaviour of time-continuous processes. Stoch. Process. Appl. (2018). https://doi.org/10.1016/j.spa.2017.12.005MathSciNetCrossRef

16.

Ikonomovska, E., Gama, J., Džeroski, S.: Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing 150, 458–470 (2015)CrossRef

17.

Jaworski, M., Duda, P., Rutkowski, L.: On applying the restricted Boltzmann machine to active concept drift detection. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2017)

18.

Liu, A., Zhang, G., Lu, J.: Fuzzy time windowing for gradual concept drift adaptation. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2017)

19.

Mahdi, O.A., Pardede, E., Cao, J.: Combination of information entropy and ensemble classification for detecting concept drift in data stream. In: Proceedings of the Australasian Computer Science Week Multiconference, p. 13. ACM (2018)

20.

Minku, L., Yao, X.: DDD: a new ensemble approach for dealing with concept drift. IEEE Trans. Knowl. Data Eng. 24(4), 619–633 (2012)CrossRef

21.

Notomista, G., Botsch, M.: A machine learning approach for the segmentation of driving maneuvers and its application in autonomous parking. J. Artif. Intell. Soft Comput. Res. 7(4), 243–255 (2017)CrossRef

22.

Oza, N.C.: Online bagging and boosting. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2340–2345. IEEE (2005)

23.

Pietruczuk, L., Rutkowski, L., Jaworski, M., Duda, P.: A method for automatic adjustment of ensemble size in stream data mining. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 9–15. IEEE (2016)

24.

Pietruczuk, L., Rutkowski, L., Jaworski, M., Duda, P.: How to adjust an ensemble size in stream data mining? Inf. Sci. 381, 46–54 (2017)MathSciNetCrossRef

25.

Polikar, R., Upda, L., Upda, S.S., Honavar, V.: Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 31(4), 497–508 (2001)CrossRef

26.

Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the Gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014)CrossRef

27.

Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Trans. Knowl. Data Eng. 25(6), 1272–1279 (2013)CrossRef

28.

Street, W.N., Kim, Y.: A streaming ensemble algorithm (sea) for large-scale classification. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377–382. ACM (2001)

29.

Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 226–235. ACM (2003)

30.

Woźniak, M., Połap, D., Napoli, C., Tramontana, E.: Graphic object feature extraction system based on cuckoo search algorithm. Expert Syst. Appl. 66, 20–31 (2016)CrossRef

31.

Zalasiński, M., Cpałka, K., Er, M.J.: Stability evaluation of the dynamic signature partitions over time. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 733–746. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_66CrossRef

32.

Zalasiński, M., Cpałka, K., Rakus-Andersson, E.: An idea of the dynamic signature verification based on a hybrid approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 232–246. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39384-1_21CrossRef

Title: On Ensemble Components Selection in Data Streams Scenario with Gradual Concept-Drift
Author: Piotr Duda
Publisher: Springer International Publishing
Book: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-91261-5

Electronic ISBN: 978-3-319-91262-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-91262-2_28

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