Abstract
This work describes the use of a quantum-inspired evolutionary algorithm (QIEA-R) to construct a weighted ensemble of neural network classifiers for adaptive learning in concept drift problems. The proposed algorithm, named NEVE (meaning Neuro-EVolutionary Ensemble), uses the QIEA-R to train the neural networks and also to determine the best weights for each classifier belonging to the ensemble when a new block of data arrives. After running eight simulations using two different datasets and performing two different analysis of the results, we show that NEVE is able to learn the data set and to quickly respond to any drifts on the underlying data, indicating that our model can be a good alternative to address concept drift problems. We also compare the results reached by our model with an existing algorithm, Learn++.NSE, in two different nonstationary scenarios.
Chapter PDF
Similar content being viewed by others
References
Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Machine Learning 1(3), 317–354 (1986)
Elwell, R., Polikar, R.: Incremental Learning of Concept drift in Nonstationary Environments. IEEE Transactions on Neural Networks 22(10), 1517–1531 (2011)
Kuncheva, L.I.: Classifier ensembles for changing environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004)
Kuncheva, L.I.: Classifier ensemble for detecting concept change in streaming data: Overview and perspectives. In: Proc. Eur. Conf. Artif. Intell., pp. 5–10 (2008)
Ahiskali, M.T.M., Muhlbaier, M., Polikar, R.: Learning concept drift in non-stationary environments using an ensemble of classifiers based approach. IJCNN, 3455–3462 (2008)
Oza, N.C.: Online Ensemble Learning. Dissertation, University of California, Berkeley (2001)
Abs da Cruz, A.V., Vellasco, M.M.B.R., Pacheco, M.A.C.: Quantum-inspired evo-lutionary algorithms for numerical optimization problems. In: Proceedings of the IEEE World Conference in Computational Intelligence (2006)
Abs da Cruz, A.V.: Algoritmos evolutivos com inspiração quântica para otimização de problemas com representação numérica. Ph.D. dissertation, Pontifical Catholic University – Rio de Janeiro (2007)
Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evolutionary Computation 6(6), 580–593 (2002)
Han, K.-H., Kim, J.-H.: On setting the parameters of QEA for practical applications: Some guidelines based on empirical evidence. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 427–428. Springer, Heidelberg (2003)
Han, K.-H., Kim, J.-H.: Quantum-inspired evolutionary algorithms with a new termination criterion, He gate, and two-phase scheme. IEEE Trans. Evolutionary Computation 8(2), 156–169 (2004)
Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: Proc. 7th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., pp. 377–382 (2001)
Polikar, R., Elwell, R.: Benchmark Datasets for Evaluating Concept drift/NSE Algorithms, http://users.rowan.edu/~polikar/research/NSE (last access at December 2012)
Montgomery, D.C.: Design and analysis of experiments. Wiley (2008)
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria (2012), Donwload at: www.r-project.org
Royston, P.: An extension of Shapiro and Wilk’s W test for normality to large samples. Applied Statistics 31, 115–124 (1982)
Kolter, J., Maloof, M.: Dynamic weighted majority: An ensemble method for drifting concepts. Journal of Machine Learning Research 8, 2755–2790 (2007)
Jackowski, K.: Fixed-size ensemble classifier system evolutionarily adapted to a recurring context with an unlimited pool of classifiers. Pattern Analysis and Applications (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
Escovedo, T., da Cruz, A.V.A., Vellasco, M., Koshiyama, A.S. (2013). NEVE: A Neuro-Evolutionary Ensemble for Adaptive Learning. In: Papadopoulos, H., Andreou, A.S., Iliadis, L., Maglogiannis, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2013. IFIP Advances in Information and Communication Technology, vol 412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41142-7_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-41142-7_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41141-0
Online ISBN: 978-3-642-41142-7
eBook Packages: Computer ScienceComputer Science (R0)