Definition
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 context...
Recommended Reading
Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6, 37–66.
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.
Clark, P., & Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3, 261–283.
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.
Domingos, P. (1997). Context-sensitive feature selection for lazy learners. Artificial Intelligence Review, 11, 227–253. [Aha, D. (Ed.). Special issue on lazy learning.]
Gaber, M. M., Zaslavsky, A., & Krishnaswamy, S. (2005). Mining data streams: A review. SIGMOD Rec., 34(2), 18–26.
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.
Harries, M. B., Sammut, C., & Horn, K. (1998). Extracting hidden context. Machine Learning, 32(2), 101–126.
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.
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.
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.
Kubat, M. (1989). Floating approximation in time-varying knowledge bases. Pattern Recognition Letters, 10, 223–227.
Kubat, M. (1992). A machine learning based approach to load balancing in computer networks. Cybernetics and Systems Journal.
Kubat, M. (1996). Second tier for decision trees. In Machine learning: Proceedings of the 13th international conference (pp. 293–301). California: Morgan Kaufmann.
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.
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.
Quinlan, J. R. (1990). Learning logical definitions from relations. Machine Learning, 5, 239–266.
Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann: San Mateo.
Salganicoff, M. (1993). Density adaptive learning and forgetting. In Machine learning: Proceedings of the tenth international conference (pp. 276–283). San Mateo: Morgan Kaufmann.
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.
Schlimmer, J., & Granger, R., Jr. (1986b). Incremental learning from noisy data. Machine Learning, 1(3), 317–354.
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.
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.
Turney, P., & Halasz, M. (1993). Contextual normalization applied to aircraft gas turbine engine diagnosis. Journal of Applied Intelligence, 3, 109–129.
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.
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.
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.
Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine Learning, 23, 69–101.
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Sammut, C., Harries, M. (2011). Concept Drift. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_153
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