Concept drift

In predictive analytics and machine learning, the concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions became less accurate as the times passes.

The term concept refers to the quantity you are looking to predict. More generally, it can also refer to other phenomena of interest besides the target concept, such as an input, but, in the context of concept drift, the term commonly refers to the target variable.

Examples
In a fraud detection application the target concept may be a binary attribute FRAUDULENT with values "yes" or "no" that indicates whether a given transaction is fraudulent. Or, in a weather prediction application, there may be several target concepts such as TEMPERATURE, PRESSURE, and HUMIDITY.

The behavior of the customers in an online shop may change over time. Let's say you want to predict weekly merchandise sales, and you have developed a predictive model that works to your satisfaction. The model may use inputs such as the amount of money spent on advertising, promotions you are running, and other metrics that may affect sales. What you are likely to experience is that the model will become less and less accurate over time - you will be a victim of concept drift. In the merchandise sales application, one reason for concept drift may be seasonality, which means that shopping behavior changes seasonally. You will likely have higher sales in the winter holiday season than during the summer.

Possible remedies
To prevent deterioration in prediction accuracy over time the model has to be refreshed periodically. One approach is to retrain the model using only the most recently observed samples (Widmer and Kubat, 1996). Another approach is to add new inputs which may be better at explaining the causes of the concept drift. For our sales prediction application you may be able to reduce concept drift by adding information about the season to your model. By providing information about the time of the year you will likely reduce rate of deterioration of your model, but you likely will never be able to prevent concept drift altogether. This is because actual shopping behavior does not follow any static, finite model. New factors may arise at any time that influence shopping behavior, the influence of the known factors or their interactions may change.

Concept drift cannot be avoided if you are looking to predict a complex phenomenon that is not governed by fixed laws of nature. All processes that arise from human activity, such as socioeconomic processes, and biological processes are likely to experience concept drift. Therefore, periodic retraining, also known as refreshing of your model is inescapable.

Bibliographic references

 * Carroll J. and Rosson M. B. The paradox of the active user. In J.M. Carroll (Ed.), Interfacing Thought: Cognitive Aspects of Human-Computer Interaction. Cambridge, MA, MIT Press, 1987.
 * Grabtree I. Soltysiak S. Identifying and Tracking Changing Interests. International Journal of Digital Libraries, Springer Verlag, vol. 2, 38-53.
 * Harries M. B., Sammut C., Horn K. Extracting Hidden Context, Machine Learning 32, 1998, pp. 101-126.
 * Klinkenberg, Ralf: Learning Drifting Concepts: Example Selection vs. Example Weighting. In Intelligent Data Analysis (IDA), Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift, Vol. 8, No. 3, pages 281--300, 2004.
 * Klinkenberg, Ralf. Predicting Phases in Business Cycles Under Concept Drift. In Hotho, Andreas and Stumme, Gerd (editors), Proceedings of LLWA-2003 / FGML-2003, pages 3--10, Karlsruhe, Germany, 2003.
 * Klinkenberg, Ralf and Rüping, Stefan: Concept Drift and the Importance of Examples. In Franke, Jürgen and Nakhaeizadeh, Gholamreza and Renz, Ingrid (editors), Text Mining -- Theoretical Aspects and Applications, pages 55--77, Berlin, Germany, Physica-Verlag, 2003.
 * Klinkenberg, Ralf: Using Labeled and Unlabeled Data to Learn Drifting Concepts. In Kubat, Miroslav and Morik, Katharina (editors), Workshop notes of the IJCAI-01 Workshop on \em Learning from Temporal and Spatial Data, pages 16--24, IJCAI, Menlo Park, CA, USA, AAAI Press, 2001.
 * Klinkenberg, Ralf and Joachims, Thorsten: Detecting Concept Drift with Support Vector Machines. In Langley, Pat (editor), Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pages 487--494, San Francisco, CA, USA, Morgan Kaufmann, 2000.
 * Klinkenberg, Ralf and Renz, Ingrid: Adaptive Information Filtering: Learning in the Presence of Concept Drifts. In Sahami, Mehran and Craven, Mark and Joachims, Thorsten and McCallum, Andrew (editors), Workshop Notes of the ICML/AAAI-98 Workshop \em Learning for Text Categorization, pages 33--40, Menlo Park, CA, USA, AAAI Press, 1998.
 * Koychev I. Gradual Forgetting for Adaptation to Concept Drift. In Proceedings of ECAI 2000 Workshop Current Issues in Spatio-Temporal Reasoning. Berlin, Germany, 2000, pp. 101-106
 * Koychev I. and Schwab I., Adaptation to Drifting User’s Interests, Proc. of ECML2000 Workshop: Machine Learning in New Information Age, Barcelona, Spain, 2000, pp. 39-45
 * Maloof, M.A. and Michalski, R.S. Learning Evolving Concepts Using Partial Memory Approach. Working Notes of the 1995 AAAI Fall Symposium on Active Learning, Boston, MA, pp. 70-73, 1995
 * Maloof M. and Michalski R. Selecting examples for partial memory learning. Machine Learning, 41(11), 2000, pp. 27-52.
 * Mitchell T., Caruana R., Freitag D., McDermott, J. and Zabowski D. Experience with a Learning Personal Assistant. Communications of the ACM 37(7), 1994, pp. 81-91.
 * Nasraoui O., Rojas C., and Cardona C., “ A Framework for Mining Evolving Trends in Web Data Streams using Dynamic Learning and Retrospective Validation ”, Journal of Computer Networks- Special Issue on Web Dynamics, 50(10), 1425-1652, July 2006
 * Nasraoui O., Cerwinske J., Rojas C., and Gonzalez F., "Collaborative Filtering in Dynamic Usage Environments", in Proc. of CIKM 2006 – Conference on Information and Knowledge Management, Arlington VA , Nov. 2006
 * Schlimmer J., and Granger R. Incremental Learning from Noisy Data, Machine Learning, 1(3), 1986, 317-357.
 * Scholz, Martin and Klinkenberg, Ralf: Boosting Classifiers for Drifting Concepts. In Intelligent Data Analysis (IDA), Special Issue on Knowledge Discovery from Data Streams, Vol. 11, No. 1, pages 3-28, March 2007.
 * Scholz, Martin and Klinkenberg, Ralf: An Ensemble Classifier for Drifting Concepts. In Gama, J. and Aguilar-Ruiz, J. S. (editors), Proceedings of the Second International Workshop on Knowledge Discovery in Data Streams, pages 53--64, Porto, Portugal, 2005.
 * Schwab I., Pohl W. and Koychev I. Learning to Recommend from Positive Evidence, Proceedings of Intelligent User Interfaces 2000, ACM Press, 241 - 247.
 * Widmer G. Tracking Changes through Meta-Learning, Machine Learning 27, 1997, pp. 256-286.
 * Widmer G. and Kubat M. Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 1996, pp. 69-101.

Software

 * YALE (YALE (Yet Another Learning Environment)): free open-source software for knowledge discovery, data mining, and machine learning also featuring data stream mining, learning time-varying concepts, and tracking drifting concept (if used in combination with its data stream mining plugin (formerly: concept drift plugin))
 * EDDM (EDDM (Early Drift Detection Method)): free open-source implementation of drift detection methods in Weka (machine learning).

Researchers working on concept drift problems

 * Joao Gama, University of Porto, Portugal
 * Ralf Klinkenberg, University of Dortmund, Germany
 * Ivan Koychev, Aberdeen, UK
 * Miroslav Kubat, FL, USA
 * Mark Maloof, USA
 * Olfa Nasraoui, University of Louisville, USA
 * Gerhard Widmer, Austria
 * Gladys Castillo, University of Aveiro, Portugal