Abstract
When the environment changes, as is increasingly the case when considering unending streams and long-life learning tasks, it is necessary to rely on on-line learning with the capability to adapt to changing conditions a.k.a. concept drifts. Previous works have focused on means to detect changes and to adapt to them. Ensemble methods relying on committees of base learners have been among the most successful approaches. In this paper, we introduce a new second-order learning mechanism that is able to detect relevant states of the environment in order to recognize recurring contexts and act pro-actively to concepts changes. Empirical comparisons with existing methods on well-known data sets show the advantage of the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gama, J.: Knowledge discovery from data streams. In: Citeseer (2010)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)
Klinkenberg, R.: Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis 8(3), 281–300 (2004)
Stanley, K.O.: Learning concept drift with a committee of decision trees. In: Informe técnico: UT-AI-TR-03-302. Department of Computer Sciences. University of Texas at Austin, USA (2003)
Katakis, I., Tsoumakas, G., Vlahavas, I.: Tracking recurring contexts using ensemble classifiers: an application to email filtering. Knowledge and Information Systems 22(3), 371–391 (2010)
Yang, Y., Wu, X., Zhu, X.: Mining in anticipation for concept change: Proactive-reactive prediction in data streams. Data Mining and Knowledge Discovery 13(3), 261–289 (2006)
Alippi, C., Boracchi, G., Roveri, M.: Just-In-Time Classifiers for Recurrent Concepts. IEEE Transactions on Neural Networks and Learning Systems (to be published, 2013)
Gama, J., Kosina, P.: Tracking recurring concepts with meta-learners. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds.) EPIA 2009. LNCS, vol. 5816, pp. 423–434. Springer, Heidelberg (2009)
Gomes, J.B., Sousa, P.A., Menasalvas, E.: Tracking recurrent concepts using context. Intelligent Data Analysis 16(5), 803–825 (2012)
Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: A new ensemble method for tracking concept drift. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 123–130 (2013)
Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Dynamic integration of classifiers for handling concept drift. Information Fusion 9(1) (2008)
Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., Gavaldà, R.: New ensemble methods for evolving data streams. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2009)
Scholz, M., Klinkenberg, R.: An ensemble classifier for drifting concepts. In: Proceedings of the Second International Workshop on Knowledge Discovery in Data Streams, Porto, Portugal (2005)
Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. Computational Linguistics 22(2), 249–254 (1996)
Schlimmer, J.C., Granger Jr., R.H.: Incremental learning from noisy data. Machine Learning 1(3), 317–354 (1986)
Bifet, A., Holmes, G., Pfahringer, B.: Leveraging bagging for evolving data streams. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part I. LNCS, vol. 6321, pp. 135–150. Springer, Heidelberg (2010)
Bifet, A.: Adaptive learning and mining for data streams and frequent patterns. ACM SIGKDD Explorations Newsletter 11(1), 55–56 (2009)
Baena-Garca, M., del Campo-vila, J., Fidalgo, R., Bifet, A., Gaval, R., Morales-Bueno, R.: Early drift detection method. In: Fourth International Workshop on Knowledge Discovery from Data Streams (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jaber, G., Cornuéjols, A., Tarroux, P. (2013). A New On-Line Learning Method for Coping with Recurring Concepts: The ADACC System. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_74
Download citation
DOI: https://doi.org/10.1007/978-3-642-42042-9_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42041-2
Online ISBN: 978-3-642-42042-9
eBook Packages: Computer ScienceComputer Science (R0)