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 become less accurate as time 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.

Contents 1 Examples 2 Possible remedies 3 Software 4 Datasets 4.1 Real 4.2 Other 4.3 Synthetic 4.4 Data generation frameworks 5 Projects 6 Meetings 7 Mailing list 8 Bibliographic references 8.1 Reviews 8.2 Papers 9 See also

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.

Software

• RapidMiner (formerly 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).
• MOA (Massive Online Analysis): free open-source software specific for mining data streams with concept drift. It contains a prequential evaluation method, the EDDM concept drift methods, a reader of ARFF real datasets, and artificial stream generators as SEA concepts, STAGGER, rotating hyperplane, random tree, and random radius based functions. MOA supports bi-directional interaction with Weka (machine learning).

Datasets

Real

• Elec2, electricity demand, 2 classes, 45312 instances. Reference: M.Harries, Splice-2 comparative evaluation: Electricity pricing, Technical report, The University of South Wales, 1999. Access from J.Gama webpage.
• Text mining, a collection of text mining datasets with concept drift, maintained by I.Katakis. Access
• Chess.com (online games) and Luxembourg (social survey) datasets compiled by I.Zliobaite. Access
• Airline, approximately 116 million flight arrival and departure records (cleaned and sorted) compiled by E.Ikonomovska. Reference: Data Expo 2009 Competition [1]. Access
• PAKDD'09 competition data represents the credit evaluation task. It is collected over a five year period. Unfortunately, the true labels are released only for the first part of the data. Access
• ECUE spam 2 datasets each consisting of more than 10,000 emails collected over a period of approximately 2 years by an individual. Access from S.J.Delany webpage

Other

• KDD'99 competition data contains simulated intrusions in a military network environment. It is often used as a benchmark to evaluate handling concept drift. Access

Synthetic

• Sine, Line, Plane, Circle and Boolean Data Sets, L.L.Minku, A.P.White, X.Yao, The Impact of Diversity on On-line Ensemble Learning in the Presence of Concept Drift, IEEE Transactions on Knowledge and Data Engineering, vol.22, no.5, pp. 730-742, 2010. Access from L.Minku webpage.
• SEA concepts, N.W.Street, Y.Kim, A streaming ensemble algorithm (SEA) for large-scale classification, KDD'01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001. Access from J.Gama webpage.
• STAGGER, J.C.Schlimmer, R.H.Granger, Incremental Learning from Noisy Data, Mach. Learn., vol.1, no.3, 1986.

Data generation frameworks

• L.L.Minku, A.P.White, X.Yao, The Impact of Diversity on On-line Ensemble Learning in the Presence of Concept Drift, IEEE Transactions on Knowledge and Data Engineering, vol.22, no.5, pp. 730-742, 2010. Download from L.Minku webpage.
• Lindstrom P, SJ Delany & B MacNamee (2008) Autopilot: Simulating Changing Concepts in Real Data In: Proceedings of the 19th Irish Conference on Artificial Intelligence & Cognitive Science, D Bridge, K Brown, B O'Sullivan & H Sorensen (eds.) p272-263 PDF
• Narasimhamurthy A., L.I. Kuncheva, A framework for generating data to simulate changing environments, Proc. IASTED, Artificial Intelligence and Applications, Innsbruck, Austria, 2007, 384-389 PDF Code

Projects

• INFER: Computational Intelligence Platform for Evolving and Robust Predictive Systems (2010 - 2014), Bournemouth University (UK), Evonik Industries (Germany), Research and Engineering Centre (Poland)
• HaCDAIS: Handling Concept Drift in Adaptive Information Systems (2008-2012), Eindhoven University of Technology (the Netherlands)
• KDUS: Knowledge Discovery from Ubiquitous Streams, INESC Porto and Laboratory of Artificial Intelligence and Decision Support (Portugal)
• ADEPT: Adaptive Dynamic Ensemble Prediction Techniques, University of Manchester (UK), University of Bristol (UK)
• ALADDIN: autonomous learning agents for decentralised data and information networks (2005-2010)

Meetings

• 2011
  • LEE 2011 Special Session on Learning in evolving environments and its application on real-world problems at ICMLA'11
  • HaCDAIS 2011 The 2nd International Workshop on Handling Concept Drift in Adaptive Information Systems
  • ICAIS 2011 Track on Incremental Learning
  • IJCNN 2011 Special Session on Concept Drift and Learning Dynamic Environments
  • CIDUE 2011 Symposium on Computational Intelligence in Dynamic and Uncertain Environments
• 2010
  • HaCDAIS 2010 International Workshop on Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions
  • ICMLA10 Special Session on Dynamic learning in non-stationary environments
  • SAC 2010 Data Streams Track at ACM Symposium on Applied Computing
  • SensorKDD 2010 International Workshop on Knowledge Discovery from Sensor Data
  • StreamKDD 2010 Novel Data Stream Pattern Mining Techniques
  • Concept Drift and Learning in Nonstationary Environments at IEEE World Congress on Computational Intelligence
  • MLMDS’2010 Special Session on Machine Learning Methods for Data Streams at the 10th International Conference on Intelligent Design and Applications, ISDA’10

Mailing list

Announcements, discussions, job postings related to the topic of concept drift in data mining / machine learning. Posts are moderated.

To subscribe go to the group home page: http://groups.google.com/group/conceptdrift

Bibliographic references

Many papers have been published describing algorithms for concept drift detection. A small number of representative ones are given below:

Reviews

• Zliobaite, I., Learning under Concept Drift: an Overview. Technical Report. 2009, Faculty of Mathematics and Informatics, Vilnius University: Vilnius, Lithuania. PDF
• Jiang, J., A Literature Survey on Domain Adaptation of Statistical Classifiers. 2008. PDF
• Kuncheva L.I. Classifier ensembles for detecting concept change in streaming data: Overview and perspectives, Proc. 2nd Workshop SUEMA 2008 (ECAI 2008), Patras, Greece, 2008, 5-10, PDF
• Gaber, M, M., Zaslavsky, A., and Krishnaswamy, S., Mining Data Streams: A Review, in ACM SIGMOD Record, Vol. 34, No. 1, June 2005, ISSN: 0163-5808
• Kuncheva L.I., Classifier ensembles for changing environments, Proceedings 5th International Workshop on Multiple Classifier Systems, MCS2004, Cagliari, Italy, in F. Roli, J. Kittler and T. Windeatt (Eds.), Lecture Notes in Computer Science, Vol 3077, 2004, 1-15, PDF.
• Tsymbal, A., The problem of concept drift: Definitions and related work. Technical Report. 2004, Department of Computer Science, Trinity College: Dublin, Ireland. PDF

Papers

• Kolter, J.Z. and Maloof, M.A. Dynamic Weighted Majority: An ensemble method for drifting concepts. Journal of Machine Learning Research 8:2755--2790, 2007. PDF
• 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.
• Gama J., Medas P., Castillo G., Rodrigues P.P.: Learning with Drift Detection. SBIA 2004: 286-295
• Maloof M.A. and Michalski R.S. 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.
• Schlimmer J., Granger R. Beyond Incremental Processing: Tracking Concept Drift. AAAI 1986.
• Wang H., Fan W., Yu Ph. S. and Han J. Mining concept-drifting data streams using ensemble classifiers. KDD 2003.
• Widmer G. and Kubat M. Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 1996, pp. 69-101.

See also

• Data stream mining
• Data mining
• Machine learning