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Learning with Discrete Least Squares on Multivariate Polynomial Spaces Using Evaluations at Random or Low-Discrepancy Point Sets Read chapter Read first chapter

2015 | OriginalPaper | Chapter

An Adaptive Classification Framework for Unsupervised Model Updating in Nonstationary Environments

Authors : Piero Conca, Jon Timmis, Rogério de Lemos, Simon Forrest, Heather McCracken

Published in: Machine Learning, Optimization, and Big Data

Publisher: Springer International Publishing

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Abstract

This paper introduces an adaptive framework that makes use of ensemble classification and self-training to maintain high classification performance in datasets affected by concept drift without the aid of external supervision to update the model of a classifier. The updating of the model of the framework is triggered by a mechanism that infers the presence of concept drift based on the analysis of the differences between the outputs of the different classifiers. In order to evaluate the performance of the proposed algorithm, comparisons were made with a set of unsupervised classification techniques and drift detection techniques. The results show that the framework is able to react more promptly to performance degradation than the existing methods and this leads to increased classification accuracy. In addition, the framework stores a smaller amount of instances with respect to a single-classifier approach.

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Literature
1.
Aggarwal, C.C., Watson, T.J., Ctr, R., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the Twenty-nineth International Conference on Very Large Data Bases, VLDB 2003, vol. 29, pp. 81–92. VLDB Endowment, Berlin (2003)
2.
Basu, S., Banerjee, A., Mooney, R.J.: Semi-supervised clustering by seeding. In: Proceedings of the Nineteenth International Conference on Machine Learning, ICML 2002, pp. 27–34. Morgan Kaufmann Publishers Inc., San Francisco (2002)
3.
Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: Proceedings of the Sixth SIAM International Conference on Data Mining, SDM 2006, pp. 328–339. SIAM (2006)
4.
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000) CrossRef
5.
Ditzler, G., Polikar, R.: An ensemble based incremental learning framework for concept drift and class imbalance. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, July 2010
6.
7.
Friedman, J.H., Rafsky, L.C.: Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests. Ann. Stat. 7, 697–717 (1979)MATHMathSciNetCrossRef
8.
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)CrossRef
9.
Hido, S., Idé, T., Kashima, H., Kubo, H., Matsuzawa, H.: Unsupervised change analysis using supervised learning. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 148–159. Springer, Heidelberg (2008) CrossRef
10.
Gonçalves Jr., P.M., de Carvalho Santos, S.G., Barros, R.S., Vieira, D.C.: A comparative study on concept drift detectors. Expert Syst. Appl. 41(18), 8144–8156 (2014)CrossRef
11.
Li, P., Wu, X., Hu, X.: Mining recurring concept drifts with limited labeled streaming data. ACM Trans. Intell. Syst. Technol. 3(2), 29:1–29:32 (2012)
12.
Nishida, K., Yamauchi, K., Omori, T.: ACE: Adaptive classifiers-ensemble system for concept-drifting environments. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 176–185. Springer, Heidelberg (2005) CrossRef
13.
Polikar, R.: Ensemble based systems in decision making. IEEE Circ. Syst. Mag. 6(3), 21–45 (2006)CrossRef
14.
Sahel, Z., Bouchachia, A., Gabrys, B., Rogers, P.: Adaptive mechanisms for classification problems with drifting data. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part II. LNCS (LNAI), vol. 4693, pp. 419–426. Springer, Heidelberg (2007) CrossRef
15.
Tsymbal, A.: The problem of concept drift: Definitions and related work. Technical report, Trinity College Dublin, Ireland (2004)
16.
Vargha, A., Delaney, H.D.: A critique and improvement of the “CL” common language effect size statistics of McGraw and Wong. J. Educ. Behav. Stat. 25(2), 101–132 (2000)
Metadata
Title
An Adaptive Classification Framework for Unsupervised Model Updating in Nonstationary Environments
Authors
Piero Conca
Jon Timmis
Rogério de Lemos
Simon Forrest
Heather McCracken
Copyright Year
2015
Book
Machine Learning, Optimization, and Big Data

Print ISBN: 978-3-319-27925-1

Electronic ISBN: 978-3-319-27926-8

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-27926-8_15

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