Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Concept Drift

  • Claude Sammut
  • Michael Harries
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_153



Concept drift occurs when the values of hidden variables change over time. That is, there is some unknown context for  concept learning and when that context changes, the learned concept may no longer be valid and must be updated or relearned.

Motivation and Background

Prediction in real-world domains is complicated by potentially unstable phenomena that are not known in advance to the learning system. For example, financial market behavior can change dramatically with changes in contract prices, interest rates, inflation rates, budget announcements, and political and world events. Thus, concept definitions that may have been learned in one context become invalid in a new context. This concept drift can be due to changes in context and is often directly reflected by one or more attributes. When changes in context are not reflected by any known attributes they can be said to be hidden. Hidden changes in...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6, 37–66.Google Scholar
  2. Chu, F., & Zaniolo, C. (2004). Fast and light boosting for adaptive mining of data streams. In Advances in knowledge discovery and data mining. Lecture notes in computer science (Vol. 3056, pp. 282–292). Springer.Google Scholar
  3. Clark, P., & Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3, 261–283.Google Scholar
  4. Clearwater, S., Cheng, T.-P., & Hirsh, H. (1989). Incremental batch learning. In Proceedings of the sixth international workshop on machine learning (pp. 366–370). Morgan Kaufmann.Google Scholar
  5. Domingos, P. (1997). Context-sensitive feature selection for lazy learners. Artificial Intelligence Review, 11, 227–253. [Aha, D. (Ed.). Special issue on lazy learning.]Google Scholar
  6. Gaber, M. M., Zaslavsky, A., & Krishnaswamy, S. (2005). Mining data streams: A review. SIGMOD Rec., 34(2), 18–26.Google Scholar
  7. Harries, M., & Horn, K. (1996). Learning stable concepts in domains with hidden changes in context. In M. Kubat & G. Widmer (Eds.), Learning in context-sensitive domains (workshop notes). 13th international conference on machine learning, Bari, Italy.Google Scholar
  8. Harries, M. B., Sammut, C., & Horn, K. (1998). Extracting hidden context. Machine Learning, 32(2), 101–126.MATHGoogle Scholar
  9. Hulten, G., Spencer, L., & Domingos, P. (2001). Mining time-changing data streams. In KDD ’01: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (pp. 97–106). New York: ACM.Google Scholar
  10. Kilander, F., & Jansson, C. G. (1993). COBBIT – A control procedure for COBWEB in the presence of concept drift. In P. B. Brazdil (Ed.), European conference on machine learning (pp. 244–261). Berlin: Springer.Google Scholar
  11. Kolter, J. Z., & Maloof, M. A. (2003). Dynamic weighted majority: A new ensemble method for tracking concept drift. In Third IEEE international conference on data mining ICDM-2003 (pp. 123–130). IEEE CS Press.Google Scholar
  12. Kubat, M. (1989). Floating approximation in time-varying knowledge bases. Pattern Recognition Letters, 10, 223–227.MATHGoogle Scholar
  13. Kubat, M. (1992). A machine learning based approach to load balancing in computer networks. Cybernetics and Systems Journal.Google Scholar
  14. Kubat, M. (1996). Second tier for decision trees. In Machine learning: Proceedings of the 13th international conference (pp. 293–301). California: Morgan Kaufmann.Google Scholar
  15. Kubat, M., & Widmer, G. (1995). Adapting to drift in continuous domains. In Proceedings of the eighth European conference on machine learning (pp. 307–310). Berlin: Springer.Google Scholar
  16. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., & Euler, T. (2006). Yale: Rapid prototyping for complex data mining tasks. In KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 935–940). New York: ACM.Google Scholar
  17. Quinlan, J. R. (1990). Learning logical definitions from relations. Machine Learning, 5, 239–266.Google Scholar
  18. Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann: San Mateo.Google Scholar
  19. Salganicoff, M. (1993). Density adaptive learning and forgetting. In Machine learning: Proceedings of the tenth international conference (pp. 276–283). San Mateo: Morgan Kaufmann.Google Scholar
  20. Schlimmer, J. C., & Granger, R. I., Jr. (1986a). Beyond incremental processing: Tracking concept drift. In Proceedings AAAI-86 (pp. 502–507). Los Altos: Morgan Kaufmann.Google Scholar
  21. Schlimmer, J., & Granger, R., Jr. (1986b). Incremental learning from noisy data. Machine Learning, 1(3), 317–354.Google Scholar
  22. Turney, P. D. (1993a). Exploiting context when learning to classify. In P. B. Brazdil (Ed.), European conference on machine learning (pp. 402–407). Berlin: Springer.Google Scholar
  23. Turney, P. D. (1993b). Robust classification with context sensitive features. In Paper presented at the industrial and engineering applicatións of artificial intelligence and expert systems.Google Scholar
  24. Turney, P., & Halasz, M. (1993). Contextual normalization applied to aircraft gas turbine engine diagnosis. Journal of Applied Intelligence, 3, 109–129.Google Scholar
  25. Wang, H., Fan, W., Yu, P. S., & Han, J. (2003). Mining concept-drifting data streams using ensemble classifiers. In KDD ’03: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 226–235). New York: ACM.Google Scholar
  26. Widmer, G. (1996). Recognition and exploitation of contextual clues via incremental meta-learning. In L. Saitta (Ed.), Machine learning: Proceedings of the 13th international workshop (pp. 525–533). San Francisco: Morgan Kaufmann.Google Scholar
  27. Widmer, G., & Kubat, M. (1993). Effective learning in dynamic environments by explicit concept tracking. In P. B. Brazdil (Ed.), European conference on machine learning (pp. 227–243). Berlin: Springer.Google Scholar
  28. Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine Learning, 23, 69–101.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Claude Sammut
  • Michael Harries

There are no affiliations available