Abstract
This work address data stream mining from dynamic environments where the distribution underlying the observations may change over time. In these contexts, learning algorithms must be equipped with change detection mechanisms. Several methods have been proposed able to detect and react to concept drift. When a drift is signaled, most of the approaches use a forgetting mechanism, by releasing the current model, and start learning a new decision model, Nevertheless, it is not rare for the concepts from history to reappear, for example seasonal changes. In this work we present method that memorizes learnt decision models whenever a concept drift is signaled. The system uses meta-learning techniques that characterize the domain of applicability of previous learnt models. The meta-learner can detect re-occurrence of contexts and take pro-active actions by activating previous learnt models. The main benefit of this approach is that the proposed meta-learner is capable of selecting similar historical concept, if there is one, without the knowledge of true classes of examples.
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., Fernandes, R., Rocha, R.: Decision Trees for Mining Data Streams. In: Intelligent Data Analysis, pp. 23–45. IOS Press, Amsterdam (2006)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004)
Gama, J., Gaber, M.M. (eds.): Learning from Data Streams: Processing Techniques in Sensor Networks. Springer, Heidelberg (2007)
Katakis, I., Tsoumakas, G., Vlahavas, I.: An Ensemble of Classifiers for coping with Reurring Contexts in Data Streams. In: 18th European Conference on Artificial Intelligence. IOS Press, Patras (2008)
Kirkby, R.: Massive Online Analysis. University of Waikato, Hamilton, New Zealand (2007), http://sourceforge.net/projects/moa-datastream/
Lazarescu, M.: A Multi-resolution Learning Approach to Tracking Concept Drift and Recurrent Concepts. PRIS (2005)
Gaber, M., Gama, J., Ganguly, A., Omitaomu, O., Vatsavai, R.: Knowledge Discovery from Sensor Data. Taylor & Francis, Abington (2008)
Ramamurthy, S., Bhatnagar, R.: Tracking Recurrent Concept Drift in Streaming Data Using Ensemble Classifiers. In: Proc. of the Sixth International Conference on Machine Learning and Applications, pp. 404–409 (2007)
Seewald, A.K., Fürnkranz, J.: Grading Classifiers. Austrian Research Institute for Artificial Intelligence, OEFAI-TR-2001-01, Wien, Austria (2001)
Schlimmer, J.C., Granger Jr., R.H.: Incremental Learning from Noisy Data. Machine Learning 1, 317–354 (1986)
Stanley, K.O.: Learning concept drift with a committee of decision trees. Tech. Report UT-AI-TR-03-302, Department of Computer Sciences, University of Texas at Austin, USA (2003)
Nick Street, W., Kim, Y.: A streaming ensemble algorithm SEA for large-scale classification. In: Proc. seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 377–382. ACM Press, New York (2001)
Tsymbal, A.: The Problem of Concept Drift: Definitions and Related Work, http://citeseer.ist.psu.edu/tsymbal04problem.html
Wang, H., Fan, W., Yu, P., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proc. KDD (2003)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques With Java Implementations. Morgan Kaufmann Publishers, San Francisco (1999)
Yang, Y., Wu, X., Zhu, X.: Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams. In: Proc. 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 710–715 (2005)
Stolfo, S., Fan, W., Lee, W., Prodromidis, A., Chan, P.: Cost-based Modeling for Fraud and Intrusion Detection, DARPA Information Survivability Conference, pp. 130–144. IEEE Computer Society Press, Los Alamitos (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gama, J., Kosina, P. (2009). Tracking Recurring Concepts with Meta-learners. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds) Progress in Artificial Intelligence. EPIA 2009. Lecture Notes in Computer Science(), vol 5816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04686-5_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-04686-5_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04685-8
Online ISBN: 978-3-642-04686-5
eBook Packages: Computer ScienceComputer Science (R0)