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Erschienen in:
Computer Based Stylometric Analysis of Texts in Polish Language Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Heuristic Regression Function Estimation Methods for Data Streams with Concept Drift

verfasst von : Maciej Jaworski, Piotr Duda, Leszek Rutkowski, Patryk Najgebauer, Miroslaw Pawlak

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

In this paper the regression function methods based on Parzen kernels are investigated. Both the modeled function and the variance of noise are assumed to be time-varying. The commonly known kernel estimator is extended by adopting two popular tools often applied in concept drifting data stream scenario. The first tool is a sliding window, in which only a constant number of recently received data elements affects the estimator. The second one is the forgetting factor. In this case at each time step past data become less and less important. These heuristic approaches are experimentally compared with the basic mathematically justified estimator and demonstrate similar accuracy.

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Vorheriges Kapitel Classifier Concept Drift Detection and the Illusion of Progress
Literatur
1.
Aggarwal, C.C.: Data Streams: Models and Algorithms. Advances in Database Systems. Springer, New York (2006)MATH
2.
Alippi, C., Boracchi, G., Roveri, M.: Hierarchical change-detection tests. IEEE Trans. Neural Netw. Learn. Syst. 28(2), 246–258 (2017)CrossRef
3.
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)
4.
Bilski, J., Smolag, J.: Parallel architectures for learning the RTRN and Elman dynamic neural networks. IEEE Trans. Parallel Distrib. Syst. 26(9), 2561–2570 (2015)CrossRef
5.
Brzezinski, D., Stefanowski, J.: Reacting to different types of concept drift: the accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 81–94 (2014)CrossRef
6.
Devroye, L.P.: On the pointwise and the integral convergence of recursive kernel estimates of probability densities. Utilitas Math. (Canada) 15, 113–128 (1979)MathSciNetMATH
7.
Ditzler, G., Roveri, M., Alippi, C., Polikar, R.: Learning in nonstationary environments: a survey. IEEE Comput. Intell. Mag. 10(4), 12–25 (2015)CrossRef
8.
Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)
9.
Duda, P., Hayashi, Y., Jaworski, M.: On the strong convergence of the orthogonal series-type kernel regression neural networks in a non-stationary environment. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS, vol. 7267, pp. 47–54. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-29347-4_​6 CrossRef
10.
Duda, P., Jaworski, M., Pietruczuk, L.: On pre-processing algorithms for data stream. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS, vol. 7268, pp. 56–63. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-29350-4_​7 CrossRef
11.
Ellis, P.: The time-dependent mean and variance of the non-stationary Markovian infinite server system. J. Math. Stat. 6, 68–71 (2010)CrossRefMATH
12.
Epanechnikov, V.A.: Non-parametric estimation of a multivariate probability density. Theory Probab. Appl. 14(1), 153–158 (1969)MathSciNetCrossRefMATH
13.
Er, M.J., Duda, P.: On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011. LNCS, vol. 7203, pp. 443–450. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-31464-3_​45 CrossRef
14.
Galkowski, T., Rutkowski, L.: Nonparametric recovery of multivariate functions with applications to system identification. Proc. IEEE 73(5), 942–943 (1985)CrossRef
15.
Galkowski, T., Rutkowski, L.: Nonparametric fitting of multivariate functions. IEEE Trans. Autom. Control 31(8), 785–787 (1986)CrossRefMATH
16.
Gałkowski, T.: Kernel estimation of regression functions in the boundary regions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7895, pp. 158–166. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-38610-7_​15 CrossRef
17.
Galkowski, T., Pawlak, M.: Nonparametric extension of regression functions outside domain. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 518–530. Springer, Cham (2014). doi:10.​1007/​978-3-319-07173-2_​44 CrossRef
18.
Galkowski, T., Pawlak, M.: Nonparametric function fitting in the presence of nonstationary noise. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 531–538. Springer, Cham (2014). doi:10.​1007/​978-3-319-07173-2_​45 CrossRef
19.
Galkowski, T., Pawlak, M.: Orthogonal series estimation of regression functions in nonstationary conditions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 427–435. Springer, Cham (2015). doi:10.​1007/​978-3-319-19324-3_​39 CrossRef
20.
Galkowski, T., Pawlak, M.: Nonparametric estimation of edge values of regression functions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 49–59. Springer, Cham (2016). doi:10.​1007/​978-3-319-39384-1_​5
21.
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)CrossRefMATH
22.
Greblicki, W., Krzyzak, A., Pawlak, M.: Distribution-free pointwise consistency of kernel regression estimate. Ann. Stat. 12, 1570–1575 (1984)MathSciNetCrossRefMATH
23.
Greblicki, W., Pawlak, M.: Necessary and sufficient consistency conditions for a recursive kernel regression estimate. J. Multivar. Anal. 23(1), 67–76 (1987)MathSciNetCrossRefMATH
24.
Greblicki, W., Pawlak, M.: Nonparametric System Identification. Cambridge University Press, Cambridge (2008)CrossRefMATH
25.
Györfi, L., Kohler, M., Krzyzak, A., Walk, H.: A Distribution-free Theory of Nonparametric Regression. Springer Science & Business Media, New York (2006)MATH
26.
Jaworski, M., Er, M.J., Pietruczuk, L.: On the application of the parzen-type kernel regression neural network and order statistics for learning in a non-stationary environment. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS, vol. 7267, pp. 90–98. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-29347-4_​11 CrossRef
27.
Jaworski, M., Pietruczuk, L., Duda, P.: On resources optimization in fuzzy clustering of data streams. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS, vol. 7268, pp. 92–99. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-29350-4_​11 CrossRef
28.
Krzyzak, A., Pawlak, M.: Almost everywhere convergence of a recursive regression function estimate and classification. IEEE Trans. Inf. Theory 30(1), 91–93 (1984)CrossRefMATH
29.
Krzyzak, A., Pawlak, M.: The pointwise rate of convergence of the kernel regression estimate. J. Stat. Plann. Infer. 16, 159–166 (1987)MathSciNetCrossRefMATH
30.
Nikulin, V.: Prediction of the shoppers loyalty with aggregated data streams. J. Artif. Intell. Soft Comput. Res. 6(2), 69–79 (2016)CrossRef
31.
32.
Pietruczuk, L., Rutkowski, L., Jaworski, M., Duda, P.: The Parzen kernel approach to learning in non-stationary environment. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 3319–3323 (2014)
33.
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 (2016)
34.
Pietruczuk, L., Duda, P., Jaworski, M.: Adaptation of decision trees for handling concept drift. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7894, pp. 459–473. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-38658-9_​41 CrossRef
35.
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
36.
Rao, B.P.: Nonparametric Functional Estimation. Academic Press, Orlando (2014)
37.
Rutkowski, L.: Generalized regression neural networks in time-varying environment. IEEE Trans. Neural Netw. 15, 576–596 (2004)CrossRef
38.
Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision tree for mining data streams. Inf. Sci. 266, 1–15 (2014)CrossRefMATH
39.
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)CrossRefMATH
40.
Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: A new method for data stream mining based on the misclassification error. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1048–1059 (2015)MathSciNetCrossRef
41.
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
42.
Serdah, A.M., Ashour, W.M.: Clustering large-scale data based on modified affinity propagation algorithm. J. Artif. Intell. Soft Comput. Res. 6(1), 23–33 (2016)CrossRef
43.
Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109–118 (1990)CrossRef
44.
Specht, D.F.: Probabilistic neural networks and the polynomial adaline as complementary techniques for classification. IEEE Trans. Neural Netw. 1(1), 111–121 (1990)CrossRef
45.
Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)CrossRef
46.
Wong, K.F.K., Galka, A., Yamashita, O., Ozaki, T.: Modelling non-stationary variance in eeg time series by state space garch model. Comput. Biol. Med. 36(12), 1327–1335 (2006)CrossRef
47.
Zliobaite, I., Bifet, A., Pfahringer, B., Holmes, G.: Active learning with drifting streaming data. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 27–39 (2014)CrossRef
Metadaten
Titel
Heuristic Regression Function Estimation Methods for Data Streams with Concept Drift
verfasst von
Maciej Jaworski
Piotr Duda
Leszek Rutkowski
Patryk Najgebauer
Miroslaw Pawlak
Copyright-Jahr
2017
Buch
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-59059-2

Electronic ISBN: 978-3-319-59060-8

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-59060-8_65

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