Abstract
Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
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- I. Adae and M. Berthold. 2013. EVE: a framework for event detection. Evolving Syst. 4, 1 (2013), 61--70.Google ScholarCross Ref
- G. Adomavicius and A. Tuzhilin. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowl. Data Eng. 17, 6 (2005), 734--749. Google ScholarDigital Library
- C. Aggarwal. 2005. On Change Diagnosis in Evolving Data Streams. IEEE Trans. Knowl. Data Eng. 17, 5 (2005), 587--600. Google ScholarDigital Library
- C. Aggarwal. 2006. On Biased Reservoir Sampling in the Presence of Stream Evolution. In Proc. of the 32nd Int. Conf. on Very Large Data Bases (VLDB). 607--618. Google ScholarDigital Library
- R. Agrawal, S. P. Ghosh, T. Imielinski, B. R. Iyer, and A. N. Swami. 1992. An Interval Classifier for Database Mining Applications. In Proc. of the 18th Int. Conf. on Very Large Data Bases (VLDB). Morgan Kaufmann, 560--573. Google ScholarDigital Library
- R. Agrawal, T. Imielinski, and A. Swami. 1993. Database Mining: A Performance Perspective. IEEE Trans. on Knowl. and Data Eng. 5, 6 (1993), 914--925. Google ScholarDigital Library
- M. Al-Kateb, L. Byung Suk, and X. Wang. 2007. Adaptive-Size Reservoir Sampling over Data Streams. In Proc. of Int. Conf. on Scientific and Statistical Database Management (SSBDM). IEEE, 22. Google ScholarDigital Library
- D. Alberg, M. Last, and A. Kandel. 2012. Knowledge Discovery in Data Streams with Regression Tree Methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, 1 (2012), 69--78.Google ScholarCross Ref
- H. H. Ang, V. Gopalkrishnan, I. Zliobaite, M. Pechenizkiy, and S. C. H. Hoi. 2013. Predictive Handling of Asynchronous Concept Drifts in Distributed Environments. IEEE Trans. on Knowl. and Data Eng. 25, 10 (2013), 2343--2355. DOI:http://dx.doi.org/10.1109/TKDE.2012.172 Google ScholarDigital Library
- B. Babcock, S. Babu, M. Datar, R. Motwani, and J. Widom. 2002. Models and Issues in Data Stream Systems. In Proc. of the 21st SIGMOD-SIGACT-SIGART Symp. on Princ. of Database Syst. (PODS). ACM, New York, NY, 1--16. Google ScholarDigital Library
- S. H. Bach and M. A. Maloof. 2008. Paired Learners for Concept Drift. In Proc. of the 8th IEEE Int. Conf. on Data Mining (ICDM). IEEE, 23--32. Google ScholarDigital Library
- K. Bache and M. Lichman. 2013. UCI Machine Learning Repository. Technical Report. University of California, Irvine. http://archive.ics.uci.edu/ml.Google Scholar
- M. Basseville and I. Nikiforov. 1993. Detection of Abrupt Changes - Theory and Application. online, France. Google ScholarDigital Library
- R. J. Bessa, V. Miranda, and J. Gama. 2009. Entropy and Correntropy against Minimum Square Error in Off-Line and On-Line 3-day ahead Wind Power Forecasting. IEEE Trans. Power Syst. 24, 4 (2009), 1657--1666.Google ScholarCross Ref
- A. Bifet and E. Frank. 2010. Sentiment Knowledge Discovery in Twitter Streaming Data. In Proc. of the 13th Int. Conf. on Discovery Science (DS). Springer-Verlag, Berlin, 1--15. Google ScholarDigital Library
- A. Bifet and R. Gavalda. 2006. Kalman Filters and Adaptive Windows for Learning in Data Streams. In Proc. of the 9th Int. Conf. on Discovery science (DS). Springer-Verlag, Germany, 29--40. Google ScholarDigital Library
- A. Bifet and R. Gavalda. 2007. Learning from Time-Changing Data with Adaptive Windowing. In Proc. of SIAM Int. Conf. on Data Mining (SDM). SIAM, 443--448.Google Scholar
- A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer. 2011. DATA STREAM MINING: A Practical Approach. Tech. rep. University of Waikato. Retrieved from http://heanet.dl.sourceforge.net/project/moa-datastream/documentation/StreamMining.pdf.Google Scholar
- A. Bifet, G. Holmes, and B. Pfahringer. 2010. Leveraging Bagging for Evolving Data Streams. In Proc. of the Eur. Conf. on Mach. Learn. and Knowledge Discovery in Databases (ECMLPKDD). Springer-Verlag, Berlin, 135--150. Google ScholarDigital Library
- A. Bifet, G. Holmes, B. Pfahringer, and E. Frank. 2010. Fast Perceptron Decision Tree Learning from Evolving Data Streams. In Proc. of the 14th PA Conf. on Knowl. Discov. and Data Mining. Springer-Verlag, Berlin, 299--310. Google ScholarDigital Library
- A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, and R. Gavalda. 2009. New ensemble methods for evolving data streams. In Proc. of the Int. Conf. on Knowl. Discov. and Data Mining. ACM, USA, 139--148. Google ScholarDigital Library
- A. Bifet, G. Holmes, B. Pfahringer, J. Read, P. Kranen, H. Kremer, T. Jansen, and T. Seidl. 2011. MOA: A Real-Time Analytics Open Source Framework. In Proc. Eur. Conf. on Mach. Learn. and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD). Springer-Verlag, Berlin, 617--620. Google ScholarDigital Library
- D. Billsus and M. J. Pazzani. 2000. User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction 10, 2--3 (2000), 147--180. Google ScholarDigital Library
- A. Blum. 1997. Empirical Support for Winnow and Weighted-MajorityAlgorithms: Results on a Calendar Scheduling Domain. Mach. Learn. 26, 1 (1997), 5--23. Google ScholarDigital Library
- J. Bobadilla, F. Ortega, A. Hernando, and A. GutiéRrez. 2013. Recommender Systems Survey. Know.-Based Syst. 46 (2013), 109--132. DOI:http://dx.doi.org/10.1016/j.knosys.2013.03.012 Google ScholarDigital Library
- R. P. J. C. Bose, W. M. P. van der Aalst, I. Zliobaite, and M. Pechenizkiy. 2014. Dealing with Concept Drift in Process Mining. IEEE Trans. Neur. Net. and Lear. Syst. 25, 1, 154--171.Google ScholarCross Ref
- A. Bouchachia. 2011a. Fuzzy Classification in Dynamic Environments. Soft Comput. 15, 5 (2011), 1009--1022. Google ScholarDigital Library
- A. Bouchachia. 2011b. Incremental Learning with Multi-Level Adaptation. Neurocomp. 74, 11 (2011), 1785--1799. Google ScholarDigital Library
- A. Bouchachia, M. Prossegger, and H. Duman. 2010. Semi-Supervised Incremental Learning. In Proc. of the IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE). IEEE, 1--6.Google Scholar
- A. Bouchachia and C. Vanaret. 2013. GT2FC: An Online Growing Interval Type-2 Self-Learning Fuzzy Classifier. IEEE Trans. Fuzzy Syst. In press. DOI:http://dx.doi.org/10.1109/TFUZZ.2013.2279554Google Scholar
- L. Breiman. 1999. Pasting Small Votes for Classification in Large Databases and On-Line. Mach. Learn. 36 (1999), 85--103. Google ScholarDigital Library
- L. Breiman and others. 1984. Classification and Regression Trees. Chapman & Hall, New York.Google Scholar
- J. Bremnes. 2004. Probabilistic Wind Power Forecasts Using Local Quantile Regression. Wind Energy 7, 1 (2004), 47--54.Google ScholarCross Ref
- J. Carmona and R. Gavaldà. 2012. Online Techniques for Dealing with Concept Drift in Process Mining. In Proc. 11th Int. Symp. Advances in Intelligent Data Analysis XI. Springer, Berlin, 90--102. Google ScholarDigital Library
- J. Carmona-Cejudo, M. Baena-Garcia, J. del Campo-Avila, R. Bueno, and A. Bifet. 2010. GNUsmail: Open Framework for On-line Email Classification. In Proc. of the 19th Eur. Conf. on Art. Intell. (ECAI). IOS Press, The Netherlands, 1141--1142. Google ScholarDigital Library
- G. A. Carpenter, S. Grossberg, and J. H. Reynolds. 1991a. ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network. Neural Networks 4 (1991), 565--588. Google ScholarDigital Library
- G. Carpenter, S. Grossberg, and D. Rosen. 1991b. Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System. Neural Networks 4, 6 (1991), 759--771. Google ScholarDigital Library
- V. Carvalho and W. Cohen. 2006. Single-Pass Online Learning: Performance, Voting Schemes and Online Feature Selection. In Proc. of the 12th ACM SIGKDD Int. Conf. on Knowl. Disc. and Data Mining (KDD). ACM, 548--553. Google ScholarDigital Library
- G. Castillo, J. Gama, and A. Breda. 2003. Adaptive Bayes for a Student Modeling Prediction Task Based on Learning Styles. In Proc. of the 9th Int. Conf. on User Modeling (UM). Springer, Berlin, 328--332. Google ScholarDigital Library
- N. Cesa-Bianchi and G. Lugosi. 2006. Prediction, Learning, and Games. Cambridge University Press, Cambridge, UK. Google ScholarDigital Library
- V. Chandola, A. Banerjee, and V. Kumar. 2009. Anomaly Detection: A Survey. ACM Comput. Surv. 41, 3 (2009), 15:1--15:58. Google ScholarDigital Library
- F. Chu and C. Zaniolo. 2004. Fast and Light Boosting for Adaptive Mining of Data Streams. In Proc. of the 5th Pac.-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD). Springer-Verlag, Berlin, 282--292.Google Scholar
- T. Dasu, Sh. Krishnan, S. Venkatasubramanian, and K. Yi. 2006. An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams. In Proc. of the 38th Symp. on the Interface of Statistics, Computing Science, and Applications.Google Scholar
- M. Datar, A. Gionis, P. Indyk, and R. Motwani. 2002. Maintaining Stream Statistics over Sliding Windows. SIAM J. Comput. 31, 6 (2002), 1794--1813. Google ScholarDigital Library
- S. Delany, P. Cunningham, A. Tsymbal, and L. Coyle. 2005. A Case-based Technique for Tracking Concept Drift in Spam filtering. Knowledge-Based Sys. 18, 4--5 (2005), 187--195. Google ScholarDigital Library
- J. Demsar. 2006. Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res. 7 (2006), 1--30. Google ScholarDigital Library
- P. Domingos and G. f. Hulten. 2000. Mining High-Speed Data Streams. In Proc. of the 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD). ACM, 71--80. Google ScholarDigital Library
- A. Dries and U. Ruckert. 2009. Adaptive Concept Drift Detection. Stat. Anal. Data Min. 2, 5--6 (2009), 311--327. Google ScholarDigital Library
- R. O. Duda, P. E. Hart, and D. G. Stork. 2001. Pattern Classification. Wiley. Google ScholarDigital Library
- C. W. Dunnett. 1955. A Multiple Comparison Procedure for Comparing Several Treatments with a Control. J. Am. Statist. Assoc. 50 (1955), 1096--1121. Issue 272.Google ScholarCross Ref
- P. Efraimidis and P. Spirakis. 2006. Weighted Random Sampling with a Reservoir. Inf. Proc. Lett. 97, 5 (2006), 181--185. Google ScholarDigital Library
- R. Elwell and R. Polikar. 2011. Incremental Learning of Concept Drift in Nonstationary Environments. IEEE Trans. on Neural Networks 22, 10 (2011), 1517--1531. Google ScholarDigital Library
- A. Fern and R. Givan. 2003. Online Ensemble Learning: An Empirical Study. Mach. Learn. 53, 1--2 (2003), 71--109. Google ScholarDigital Library
- G. Forman. 2006. Tackling concept drift by temporal inductive transfer. In Proc. of the 29th Int. ACM SIGIR Conf. on Research and Development in Inf. Retrieval (SIGIR). ACM, USA, 252--259. Google ScholarDigital Library
- R. M. French. 1994. Catastrophic Forgetting in Connectionist Networks: Causes, Consequences and Solutions. Trends Cognit. Sciences 3, 4 (1994), 128--135.Google ScholarCross Ref
- M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy. 2005. Mining Data Streams: A Review. SIGMOD Rec. 34, 2 (June 2005), 18--26. Google ScholarDigital Library
- J. Gama. 2010. Knowledge Discovery from Data Streams. Chapman & Hall/CRC, London. Google ScholarDigital Library
- J. Gama, R. Fernandes, and R. Rocha. 2006. Decision Trees for Mining Data Streams. Intelligent Data Analysis 10, 1 (2006), 23--46. Google ScholarDigital Library
- J. Gama and P. Kosina. 2011. Learning about the Learning Process. In Proc. of the 10th Int. Conf. on Advances in Intelligent Data Analysis (IDA). Springer, Berlin, 162--172. Google ScholarDigital Library
- J. Gama, P. Medas, G. Castillo, and P. Rodrigues. 2004. Learning with Drift Detection. In Proc. of the 17th Brazilian Symp. on Artif. Intell. (SBIA). Springer, Berlin, 286--295.Google Scholar
- J. Gama, R. Sebastião, and P. P. Rodrigues. 2013. On evaluating stream learning algorithms. Mach. Learn. 90, 3 (2013), 317--346. Google ScholarDigital Library
- J. Gantz and D. Reinsel. 2012. IDC: The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. (December 2012).Google Scholar
- J. Gao, W. Fan, J. Han, and P. S. Yu. 2007. A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions. In Proc. of the 7th SIAM Int. Conf. on Data Mining (SDM). SIAM, USA.Google Scholar
- J. Gehrke, R. Ramakrishnan, and V. Ganti. 2000. RainForest—A Framework for Fast Decision Tree Construction of Large Datasets. Data Mining and Knowl. Discovery 4 (2000), 127--162. Issue 2--3. Google ScholarDigital Library
- C. Giraud-Carrier. 2000. A note on the utility of incremental learning. AI Commun. 13, 4 (Dec. 2000), 215--223. Google ScholarDigital Library
- J. B. Gomes, E. M. Ruiz, and P. A. C. Sousa. 2011. Learning Recurring Concepts from Data Streams with a Context-Aware Ensemble. In Proc. of the ACM Symp. on Appl. Comp. (SAC). ACM, USA, 994--999. Google ScholarDigital Library
- A.-M. Grisogono. 2006. The Implications of Complex Adaptive Systems Theory for C2. In State of the Art State of the Practice, Vol. CCRTS. Defense Technical Information Center.Google Scholar
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. 2009. The WEKA Data Mining Software: An Update. SIGKDD Explor. Newsl. 11, 1 (2009), 10--18. Google ScholarDigital Library
- M. Harries. 1999. SPLICE-2 Comparative Evaluation: Electricity Pricing. Tech. rep. South Wales Univ.Google Scholar
- M. Harries, C. Sammut, and K. Horn. 1998. Extracting Hidden Context. Machine Learning 32 (1998), 101--126. Issue 2. Google ScholarDigital Library
- D. P. Helmbold and P. M. Long. 1994. Tracking Drifting Concepts By Minimizing Disagreements. Mach. Learn. 14, 1 (Jan. 1994), 27--45. Google ScholarDigital Library
- M. Herbster and M. Warmuth. 1998. Tracking the Best Expert. Mach. Learn. 32, 2 (1998), 151--178. Google ScholarDigital Library
- G. Hulten, L. Spencer, and P. Domingos. 2001. Mining Time-Changing Data Streams. In Proc. of the 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD). ACM, 97--106. Google ScholarDigital Library
- E. Ikonomovska, J. Gama, and S. Dzeroski. 2011. Learning Model Trees from Evolving Data Streams. Data Mining Knowl.e Discovery 23, 1 (2011), 128--168. Google ScholarDigital Library
- T. Joachims. 2000. Estimating the Generalization Performance of an SVM Efficiently. In Proc. of the 17th Int. Conf. on Mach. Learn. (ICML). Morgan Kaufmann Publishers, USA, 431--438. Google ScholarDigital Library
- P. Kadlec, R. Grbic, and B. Gabrys. 2011. Review of Adaptation Mechanisms for Data-Driven Soft Sensors. Comput. Chem. Engin. 35, 1 (2011), 1--24.Google ScholarCross Ref
- I. Katakis, G. Tsoumakas, and I. Vlahavas. 2010. Tracking Recurring Contexts Using Ensemble Classifiers: An Application to Email Filtering. Knowl. Inf. Syst. 22, 3 (2010), 371--391. Google ScholarDigital Library
- M. G. Kelly, D. J. Hand, and N. M. Adams. 1999. The Impact of Changing Populations on Classifier Performance. In Proc. of the 5th ACM SIGKDD Int. Conf. on Knowl. Disc. and Dat. Mining (KDD). ACM, 367--371. Google ScholarDigital Library
- D. Kifer, Sh. Ben-David, and J. Gehrke. 2004. Detecting Change in Data Streams. In Proc. of the 13th Int. Conf. on Very Large Data Bases (VLDB). VLDB Endowment, 180--191. Google ScholarDigital Library
- R. Klinkenberg. 2003. Predicting Phases in Business Cycles Under Concept Drift. In Proc. of the Ann. Workshop on Machine Learning of the National German Computer Science Society (LLWA). LLWA, Germany, 3--10.Google Scholar
- R. Klinkenberg. 2004. Learning Drifting Concepts: Example Selection vs. Example Weighting. Intelligent Data Analysis 8, 3 (2004), 281--300. Google ScholarDigital Library
- R. Klinkenberg and Th. Joachims. 2000. Detecting Concept Drift with Support Vector Machines. In Proc. of the 17th Int. Conf. on Machine Learning (ICML). Morgan Kaufmann, 487--494. Google ScholarDigital Library
- R. Klinkenberg and I. Renz. 1998. Adaptive Information Filtering: Learning in the Presence of Concept Drifts. In Workshop Notes of the ICML/AAAI-98 Workshop on Learning for Text Categorization. AAAI, 33--40.Google Scholar
- J. Kolter and M. Maloof. 2003. Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift. In Proc. of the 3rd IEEE Int. Conf. on Data Mining (ICDM). IEEE, 123--130. Google ScholarDigital Library
- J. Kolter and M. Maloof. 2005. Using Additive Expert Ensembles to Cope with Concept Drift. In Proc. of the 22th Int. Conf. on Machine Learning (ICML). ACM, 449--456. Google ScholarDigital Library
- J. Kolter and M. Maloof. 2007. Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts. Journal of Machine Learning Research 8 (2007), 2755--2790. Google ScholarDigital Library
- Y. Koren. 2010. Collaborative Filtering with Temporal Dynamics. Commun. ACM 53, 4 (2010), 89--97. Google ScholarDigital Library
- P. Kosina, J. Gama, and R. Sebastiao. 2010. Drift Severity Metric. In Proc. of the 19th Eur. Conf. on Artificial Intelligence (ECAI). IOS Press, The Netherlands, 1119--1120. Google ScholarDigital Library
- I. Koychev. 2000. Gradual Forgetting for Adaptation to Concept Drift. In Proc. of ECAI Workshop on Current Issues in Spatio-Temporal Reasoning. ECAI, Germany, 101--106.Google Scholar
- I. Koychev. 2002. Tracking Changing User Interests through Prior-Learning of Context. In Proc. of the 2nd Int. Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems. Springer, Berlin, 223--232. Google ScholarDigital Library
- M. Kukar. 2003. Drifting concepts as hidden factors in clinical studies. In Proc. of AIME 2003, 9th Conference on Artificial Intelligence in Medicine in Europe. Springer, Berlin, 355--364.Google ScholarCross Ref
- L. Kuncheva. 2008. Classifier Ensembles for Detecting Concept Change in streaming data: Overview and perspectives. In Proc. of the 2nd Workshop SUEMA 2008. SUEMA, online.Google Scholar
- L. Kuncheva and I. Zliobaite. 2009. On the Window Size for Classification in Changing Environments. Intelligent Data Analysis 13, 6 (2009), 861--872. Google ScholarDigital Library
- L. I. Kuncheva. 2004. Classifier ensembles for changing environments. In Proc. of the 5th Int. Worksh. on Multiple Classifier Systems (MCS). Springer, Berlin, 1--15.Google Scholar
- L. I. Kuncheva. 2009. Using Control Charts for Detecting Concept Change in Streaming Data. Tech. rep. BCS-TR-001-2009. School of Computer Science, Bangor University, UK. Retrieved from http://www.bangor.ac.uk/∼mas00a/papers/lkTR09.pdf.Google Scholar
- L. I. Kuncheva. 2013. Change Detection in Streaming Multivariate Data Using Likelihood Detectors. IEEE Transactions on Knowledge and Data Engineering 25, 5 (2013), 1175--1180. Google ScholarDigital Library
- L. I. Kuncheva and C. O. Plumpton. 2008. Adaptive Learning Rate for Online Linear Discriminant Classifiers. In Proc. of Int. Worksh. on Structural and Syntactic Pattern Recognition (SSPR). Springer, Berlin, 510--519. Google ScholarDigital Library
- C. Lanquillon. 2002. Enhancing Text Classification to Improve Information Filtering. Künstliche Intelligenz, 16, 2 (2002), 37--38.Google Scholar
- M. M. Lazarescu, S. Venkatesh, and H. H. Bui. 2004. Using Multiple Windows to Track Concept Drift. Intelligent Data Analysis 8, 1 (2004), 29--59. Google ScholarDigital Library
- M. Leeuwen and A. Siebes. 2008. StreamKrimp: Detecting Change in Data Streams. In Proc. of the Eur. Conf. on Mach. Learn. and Knowledge Discovery in Databases (ECMLPKDD). Springer, Berlin, 672--687. Google ScholarDigital Library
- P. Lindstrom, S. J. Delany, and B. Mac Namee. 2010. Handling Concept Drift in a Text Data Stream Constrained by High Labelling Cost. In Proc. of the 23rd Int. Florida Art. Intell. Research Society Conf. FLAIRS.Google Scholar
- N. Littlestone. 1987. Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm. Machine Learning 2, 4 (1987), 285--318. Google ScholarDigital Library
- N. Littlestone and M. Warmuth. 1994. The Weighted Majority Algorithm. Inf. Comput. 108, 2 (1994), 212--261. Google ScholarDigital Library
- M. Maloof and R. Michalski. 2000. Selecting Examples for Partial Memory Learning. Machine Learning 41 (2000), 27--52. Google ScholarDigital Library
- M. Maloof and R. Michalski. 2004. Incremental Learning with Partial Instance Memory. Artificial Intelligence 154 (2004), 95--126. Google ScholarDigital Library
- M. A. Maloof. 2010. The AQ Methods for Concept Drift. In Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S. Michalski. Springer, Berlin, 23--47.Google Scholar
- M. A. Maloof and R. S. Michalski. 1995. A Method for Partial-Memory Incremental Learning and Its Application to Computer Intrusion Detection. In Proc. of the 7th IEEE Int. Conf. on Tools with Artif. Intell. IEEE, 392--397. Google ScholarDigital Library
- M. Markou and S. Singh. 2003. Novelty Detection: A Review—Part 1: Statistical Approaches. Signal Processing 83 (2003), 2481--2497. Google ScholarDigital Library
- M. Masud, J. Gao, L. Khan, J. Han, and B. Thuraisingham. 2011. Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints. IEEE TKDE 23, 6 (2011), 859--874. Google ScholarDigital Library
- Q. McNemar. 1947. Note on the Sampling Error of the Difference between Correlated Proportions or Percentages. Psychometrika 12, 2 (1947), 153--157.Google ScholarCross Ref
- M. Mehta, R. Agrawal, and J. Rissanen. 1996. SLIQ: A Fast Scalable Classifier for Data Mining. In Proc. of the 5th Int. Conf. on Extending Database Technol.: Advances in Database Technol. (EDBT). Springer, Berlin, 18--32. Google ScholarDigital Library
- L. Minku, A. White, and X. Yao. 2010. The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift. IEEE Transactions on Knowledge and Data Engineering 22 (May 2010), 730--742. Issue 5. Google ScholarDigital Library
- L. Minku and X. Yao. 2011. DDD: A New Ensemble Approach for Dealing with Concept Drift. IEEE Transactions on Knowledge and Data Engineering 24, 4 (2011), 619--633. Google ScholarDigital Library
- C. Monteiro, R. Bessa, V. Miranda, A. Botterud, J. Wang, and G. Conzelmann. 2009. Wind Power Forecasting: State-of-the-Art 2009. Tech. rep. ANL/DIS-10-1. Argonne National Laboratory.Google Scholar
- J. G. Moreno-Torres, T. Raeder, R. Alaiz-Rodriguez, N. V. Chawla, and F. Herrera. 2012. A Unifying View on Dataset Shift in Classification. Pattern Recognition 45, 1 (2012), 521--530. Google ScholarDigital Library
- H. Mouss, D. Mouss, N. Mouss, and L. Sefouhi. 2004. Test of Page-Hinkley, an Approach for Fault Detection in an Agro-Alimentary Production System. In Proc. of the Asian Control Conference. IEEE, 815--818.Google Scholar
- S. Muthukrishnan, E. van den Berg, and Y. Wu. 2007. Sequential Change Detection on Data Streams. In Proc. of the 7th IEEE Int. Conf. on Data Mining (ICDMW). IEEE, 551--550. Google ScholarDigital Library
- W. Ng and M. Dash. 2008. A Test Paradigm for Detecting Changes in Transactional Data Streams. In Proc. of the 13th Int. Conf. on Database Systems for Advanced Applications (DASFAA). Springer, Berlin, 204--219. Google ScholarDigital Library
- K. Nishida and K. Yamauchi. 2007. Detecting Concept Drift Using Statistical Testing. In Proc. of the 10th International Conference on Discovery Science (DS'07). Springer-Verlag, Berlin, 264--269. http://dl.acm.org/citation.cfm?id=1778942.1778972 Google ScholarDigital Library
- N. Oza. 2001. Online Ensemble Learning. Ph.D. Dissertation. University of California Berkeley. Google ScholarDigital Library
- E. S. Page. 1954. Continuous Inspection Schemes. Biometrika 41, 1/2 (1954), 100--115.Google ScholarCross Ref
- M. Pechenizkiy, J. Bakker, I. Zliobaite, A. Ivannikov, and T. Kärkkäinen. 2009. Online Mass Flow Prediction in CFB Boilers with Explicit Detection of Sudden Concept Drift. SIGKDD Explor. 11, 2 (2009), 109--116. Google ScholarDigital Library
- R. Polikar, L. Udpa, S. Udpa, and V. Honavar. 2001. Learn++: An Incremental Learning Algorithm for Supervised Neural Networks. IEEE Trans. on Syst., Man and Cyber. C 31 (2001), 497--508. Google ScholarDigital Library
- F. Rosenblatt. 1958. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review 65, 6 (1958), 386--408.Google ScholarCross Ref
- G. J. Ross, N. M. Adams, D. K. Tasoulis, and D. J. Hand. 2012. Exponentially Weighted Moving Average Charts for Detecting Concept Drift. Pattern Recogn. Lett. 33, 2 (Jan. 2012), 191--198. Google ScholarDigital Library
- F. Rusu and A. Dobra. 2009. Sketching Sampled Data Streams. In Proc. of the 2009 IEEE Int. Conf. on Data Eng. (ICDE). IEEE, 381--392. Google ScholarDigital Library
- M. Salganicoff. 1993. Density-Adaptive Learning and Forgetting. In Proc. of the Int. Conf. on Mach. Learn. (ICML). Morgan Kaufmann, 276--283.Google ScholarCross Ref
- M. Salganicoff. 1997. Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching. Artificial Intelligence Review 11, 1--5 (1997), 133--155. Google ScholarDigital Library
- J. Schlimmer and R. Granger. 1986. Incremental Learning from Noisy Data. Mach. Learn. 1, 3 (1986), 317--354. Google ScholarDigital Library
- M. Scholz and R. Klinkenberg. 2007. Boosting Classifiers for Drifting Concepts. Intell. Data Anal. 11, 1 (2007), 3--28. Google ScholarDigital Library
- R. Sebastião and J. Gama. 2007. Change Detection in Learning Histograms from Data Streams. In Progress in Artificial Intelligence: Proc. of the Portuguese Conf. on Art. Intell. Springer, Berlin, 112--123. Google ScholarDigital Library
- J. C. Shafer, R. Agrawal, and M. Mehta. 1996. SPRINT: A Scalable Parallel Classifier for Data Mining. In Proc. of the 22th Int. Conf. on Very Large Data Bases (VLDB). Morgan Kaufmann, 544--555. Google ScholarDigital Library
- A. Shiryaev. 2009. On Stochastic Models and Optimal Methods in the Quickest Detection Problems. Theory Probab. Appl. 53, 3 (2009), 385--401.Google ScholarCross Ref
- W. Street and Y. Kim. 2001. A Streaming Ensemble Algorithm SEA for Large-Scale Classification. In Proc. 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD). ACM, 377--382. Google ScholarDigital Library
- N. Syed, H. Liu, and K. Sung. 1999. Handling Concept Drifts in Incremental Learning with Support Vector Machines. In Proc. of the 5th ACM SIGKDD Int. Conf. on Knowl. Disc. and Data Mining (KDD). ACM, 317--321. Google ScholarDigital Library
- A. Tartakovsky and G. Moustakides. 2010. State-of-the-Art in Bayesian Changepoint Detection. Sequential Anal. 29 (2010), 125--145.Google ScholarCross Ref
- S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, K. Lau, C. Oakley, M. Palatucci, V. Pratt, P. Stang, S. Strohband, C. Dupont, L. Jendrossek, et al. 2006. Stanley: The Robot That Won the Darpa Challenge. J. Field Robot. 23, 9 (2006), 661--692. Google ScholarDigital Library
- A. Tsymbal. 2004. The Problem of Concept Drift: Definitions and Related Work. Tech. rep. Department of Computer Science, Trinity College, Dublin.Google Scholar
- A. Tsymbal, M. Pechenizkiy, P. Cunningham, and S. Puuronen. 2006. Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections. In Proc. of 19th IEEE Int. Symp. on Computer-Based Medical Syst. (CBMS). IEEE, 679--684. Google ScholarDigital Library
- W. M. P. van der Aalst. 2011. Process Mining—Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin. I--XVI, 1--352 pages. Google ScholarDigital Library
- W. M. P. van der Aalst. 2012. Process Mining. Commun. ACM 55, 8 (2012), 76--83. Google ScholarDigital Library
- J. Vitter. 1985. Random Sampling with a Reservoir. ACM Trans. Math. Softw. 11, 1 (1985), 37--57. Google ScholarDigital Library
- P. Vorburger and A. Bernstein. 2006. Entropy-based Concept Shift Detection. In Proc. of the 6th Int. Conf. on Data Mining (ICDM). IEEE, 1113--1118. Google ScholarDigital Library
- V. Vovk. 1998. A Game of Prediction with Expert Advice. J. Comput. Syst. Sci. 56, 2 (1998), 153--173. Google ScholarDigital Library
- A. Wald. 1947. Sequential Analysis. John Wiley and Sons.Google Scholar
- H. Wang, W. Fan, P. Yu, and J. Han. 2003. Mining Concept-Drifting Data Streams Using Ensemble Classifiers. In Proc. of the 9th ACM SIGKDD Int. Conf. on Knowl. Disc. and Data Mining (KDD). ACM, 226--235. Google ScholarDigital Library
- G. Widmer. 1997. Tracking Context Changes through Meta-Learning. Mach. Learn. 27, 3 (June 1997), 259--286. Google ScholarDigital Library
- G. Widmer and M. Kubat. 1993. Effective Learning in Dynamic Environments by Explicit Context Tracking. In Proc. of the Eur. Conf. on Mach. Learn. (ECML). Springer, Berlin, 227--243. Google ScholarDigital Library
- G. Widmer and M. Kubat. 1996. Learning in the Presence of Concept Drift and Hidden Contexts. Mach. Learn. 23, 1 (1996), 69--101. Google ScholarDigital Library
- Y. Yang, X. Wu, and X. Zhu. 2006. Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams. Data Mining and Knowledge Discovery 13, 3 (2006), 261--289. Google ScholarDigital Library
- R. Yao, Q. Shi, C. Shen, Y. Zhang, and A. van den Hengel. 2012. Robust Tracking with Weighted Online Structured Learning. In Proc. of the 12th Eur. Conf. on Computer Vision (ECCV). Springer, Berlin, 158--172. Google ScholarDigital Library
- G. Zeira, O. Maimon, M. Last, and L. Rokach. 2004. Change Detection in Classification Models Induced from Time-Series Data. In Data Mining in Time Series Databases. Vol. 57. World Scientific, Singapore, 101--125.Google Scholar
- P. Zhang, X. Zhu, and Y. Shi. 2008. Categorizing and Mining Concept Drifting Data Streams. In Proc. of the 14th ACM SIGKDD Int. Conf. on Knowl. Disc. and Data Mining (KDD). ACM, 812--820. Google ScholarDigital Library
- Z. Zhang and J. Zhou. 2010. Transfer Estimation of Evolving Class Priors in Data Stream Classification. Pattern Recogn. 43, 9 (2010), 3151--3161. Google ScholarDigital Library
- P. Zhao, S. Hoi, R. Jin, and T. Yang. 2011. Online AUC Maximization. In Proc. of the 28th Int. Conf. on Machine Learning (ICML). Omnipress, 233--240.Google Scholar
- I. Zliobaite. 2009. Learning under Concept Drift: An Overview. Tech. rep. Vilnius University.Google Scholar
- I. Zliobaite. 2011a. Combining Similarity in Time and Space for Training Set Formation under Concept Drift. Intell. Data Anal. 15, 4 (2011), 589--611. Google ScholarDigital Library
- I. Zliobaite. 2011b. Controlled Permutations for Testing Adaptive Classifiers. In Proc. of the 14th Int. Conf. on Discovery Science (DS). Springer, Berlin, 365--379. Google ScholarDigital Library
- I. Zliobaite, J. Bakker, and M. Pechenizkiy. 2012a. Beating the Baseline Prediction in Food Sales: How Intelligent an Intelligent Predictor Is? Expert Syst. Appl. 39, 1 (2012), 806--815. Google ScholarDigital Library
- I. Zliobaite, A. Bifet, M. M. Gaber, B. Gabrys, J. Gama, L. L. Minku, and K. Musial. 2012b. Next Challenges for Adaptive Learning Systems. SIGKDD Explorations 14, 1 (2012), 48--55. Google ScholarDigital Library
- I. Zliobaite, A. Bifet, B. Pfahringer, and G Holmes. 2014. Active Learning with Drifting Streaming Data. IEEE Trans. Neural Networks Learn. Syst. 25, 1, 27--39.Google ScholarCross Ref
- I. Zliobaite and L. Kuncheva. 2009. Determining the Training Window for Small Sample Size Classification with Concept Drift. In Proc. of IEEE Int. Conf. on Data Mining Workshops (ICDMW). IEEE, 447--452. Google ScholarDigital Library
Index Terms
- A survey on concept drift adaptation
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