A survey on concept drift adaptation.

*(English)*Zbl 1305.68141##### MSC:

68T05 | Learning and adaptive systems in artificial intelligence |

68-02 | Research exposition (monographs, survey articles) pertaining to computer science |

PDF
BibTeX
XML
Cite

\textit{J. Gama} et al., ACM Comput. Surv. 46, No. 4, Paper No. 44, 37 p. (2014; Zbl 1305.68141)

Full Text:
DOI

##### References:

[1] | I. Adae and M. Berthold. 2013. EVE: a framework for event detection. Evolving Syst. 4, 1 (2013), 61–70. |

[2] | 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. · Zbl 05109798 |

[3] | C. Aggarwal. 2005. On Change Diagnosis in Evolving Data Streams. IEEE Trans. Knowl. Data Eng. 17, 5 (2005), 587–600. · Zbl 05109594 |

[4] | 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. |

[5] | 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. |

[6] | 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. · Zbl 05109835 |

[7] | 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. |

[8] | 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. |

[9] | 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 |

[10] | 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. |

[11] | 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. |

[12] | K. Bache and M. Lichman. 2013. UCI Machine Learning Repository. Technical Report. University of California, Irvine. http://archive.ics.uci.edu/ml. |

[13] | M. Basseville and I. Nikiforov. 1993. Detection of Abrupt Changes - Theory and Application. online, France. |

[14] | 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. |

[15] | 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. |

[16] | 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. |

[17] | 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. |

[18] | 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. |

[19] | 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. |

[20] | 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. |

[21] | 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. |

[22] | 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. |

[23] | 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. |

[24] | A. Blum. 1997. Empirical Support for Winnow and Weighted-MajorityAlgorithms: Results on a Calendar Scheduling Domain. Mach. Learn. 26, 1 (1997), 5–23. · Zbl 05469151 |

[25] | 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 |

[26] | 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. |

[27] | A. Bouchachia. 2011a. Fuzzy Classification in Dynamic Environments. Soft Comput. 15, 5 (2011), 1009–1022. · Zbl 06035770 |

[28] | A. Bouchachia. 2011b. Incremental Learning with Multi-Level Adaptation. Neurocomp. 74, 11 (2011), 1785–1799. · Zbl 06017203 |

[29] | 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. |

[30] | 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.2013.2279554 |

[31] | L. Breiman. 1999. Pasting Small Votes for Classification in Large Databases and On-Line. Mach. Learn. 36 (1999), 85–103. · Zbl 05467505 |

[32] | L. Breiman and others. 1984. Classification and Regression Trees. Chapman & Hall, New York. |

[33] | J. Bremnes. 2004. Probabilistic Wind Power Forecasts Using Local Quantile Regression. Wind Energy 7, 1 (2004), 47–54. |

[34] | 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. |

[35] | 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. |

[36] | 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. |

[37] | 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. |

[38] | 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. |

[39] | 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. · Zbl 1039.68810 |

[40] | N. Cesa-Bianchi and G. Lugosi. 2006. Prediction, Learning, and Games. Cambridge University Press, Cambridge, UK. · Zbl 1114.91001 |

[41] | V. Chandola, A. Banerjee, and V. Kumar. 2009. Anomaly Detection: A Survey. ACM Comput. Surv. 41, 3 (2009), 15:1–15:58. |

[42] | 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. |

[43] | 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. |

[44] | M. Datar, A. Gionis, P. Indyk, and R. Motwani. 2002. Maintaining Stream Statistics over Sliding Windows. SIAM J. Comput. 31, 6 (2002), 1794–1813. · Zbl 1008.68039 |

[45] | 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. |

[46] | J. Demsar. 2006. Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res. 7 (2006), 1–30. |

[47] | 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. |

[48] | A. Dries and U. Ruckert. 2009. Adaptive Concept Drift Detection. Stat. Anal. Data Min. 2, 5–6 (2009), 311–327. |

[49] | R. O. Duda, P. E. Hart, and D. G. Stork. 2001. Pattern Classification. Wiley. |

[50] | 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. · Zbl 0066.12603 |

[51] | P. Efraimidis and P. Spirakis. 2006. Weighted Random Sampling with a Reservoir. Inf. Proc. Lett. 97, 5 (2006), 181–185. · Zbl 1184.68620 |

[52] | R. Elwell and R. Polikar. 2011. Incremental Learning of Concept Drift in Nonstationary Environments. IEEE Trans. on Neural Networks 22, 10 (2011), 1517–1531. |

[53] | A. Fern and R. Givan. 2003. Online Ensemble Learning: An Empirical Study. Mach. Learn. 53, 1–2 (2003), 71–109. · Zbl 1076.68548 |

[54] | 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. |

[55] | R. M. French. 1994. Catastrophic Forgetting in Connectionist Networks: Causes, Consequences and Solutions. Trends Cognit. Sciences 3, 4 (1994), 128–135. |

[56] | M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy. 2005. Mining Data Streams: A Review. SIGMOD Rec. 34, 2 (June 2005), 18–26. · Zbl 05444176 |

[57] | J. Gama. 2010. Knowledge Discovery from Data Streams. Chapman & Hall/CRC, London. · Zbl 1230.68017 |

[58] | J. Gama, R. Fernandes, and R. Rocha. 2006. Decision Trees for Mining Data Streams. Intelligent Data Analysis 10, 1 (2006), 23–46. |

[59] | 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. · Zbl 05965122 |

[60] | 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. · Zbl 1105.68376 |

[61] | J. Gama, R. Sebastião, and P. P. Rodrigues. 2013. On evaluating stream learning algorithms. Mach. Learn. 90, 3 (2013), 317–346. · Zbl 1260.68329 |

[62] | 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). |

[63] | 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. |

[64] | 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. |

[65] | C. Giraud-Carrier. 2000. A note on the utility of incremental learning. AI Commun. 13, 4 (Dec. 2000), 215–223. · Zbl 0967.68087 |

[66] | 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. |

[67] | 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. |

[68] | 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. · Zbl 05740105 |

[69] | M. Harries. 1999. SPLICE-2 Comparative Evaluation: Electricity Pricing. Tech. rep. South Wales Univ. |

[70] | M. Harries, C. Sammut, and K. Horn. 1998. Extracting Hidden Context. Machine Learning 32 (1998), 101–126. Issue 2. · Zbl 0912.68163 |

[71] | D. P. Helmbold and P. M. Long. 1994. Tracking Drifting Concepts By Minimizing Disagreements. Mach. Learn. 14, 1 (Jan. 1994), 27–45. · Zbl 0942.68667 |

[72] | M. Herbster and M. Warmuth. 1998. Tracking the Best Expert. Mach. Learn. 32, 2 (1998), 151–178. · Zbl 0912.68165 |

[73] | 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. |

[74] | 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. · Zbl 1235.68158 |

[75] | 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. |

[76] | 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. |

[77] | 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. · Zbl 05835198 |

[78] | 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. |

[79] | 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. |

[80] | 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. |

[81] | R. Klinkenberg. 2004. Learning Drifting Concepts: Example Selection vs. Example Weighting. Intelligent Data Analysis 8, 3 (2004), 281–300. |

[82] | 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. |

[83] | 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. |

[84] | 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. |

[85] | 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. |

[86] | J. Kolter and M. Maloof. 2007. Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts. Journal of Machine Learning Research 8 (2007), 2755–2790. · Zbl 1222.68237 |

[87] | Y. Koren. 2010. Collaborative Filtering with Temporal Dynamics. Commun. ACM 53, 4 (2010), 89–97. · Zbl 05748178 |

[88] | 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. |

[89] | 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. |

[90] | 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. · Zbl 1047.68823 |

[91] | 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. |

[92] | 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. |

[93] | L. Kuncheva and I. Zliobaite. 2009. On the Window Size for Classification in Changing Environments. Intelligent Data Analysis 13, 6 (2009), 861–872. |

[94] | 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. |

[95] | 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. |

[96] | 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. |

[97] | 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. · Zbl 05487460 |

[98] | C. Lanquillon. 2002. Enhancing Text Classification to Improve Information Filtering. Künstliche Intelligenz, 16, 2 (2002), 37–38. |

[99] | 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. |

[100] | 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. |

[101] | 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. |

[102] | N. Littlestone. 1987. Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm. Machine Learning 2, 4 (1987), 285–318. |

[103] | N. Littlestone and M. Warmuth. 1994. The Weighted Majority Algorithm. Inf. Comput. 108, 2 (1994), 212–261. · Zbl 0804.68121 |

[104] | M. Maloof and R. Michalski. 2000. Selecting Examples for Partial Memory Learning. Machine Learning 41 (2000), 27–52. · Zbl 02180965 |

[105] | M. Maloof and R. Michalski. 2004. Incremental Learning with Partial Instance Memory. Artificial Intelligence 154 (2004), 95–126. · Zbl 1085.68641 |

[106] | 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. · Zbl 1185.68551 |

[107] | 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. |

[108] | M. Markou and S. Singh. 2003. Novelty Detection: A Review—Part 1: Statistical Approaches. Signal Processing 83 (2003), 2481–2497. · Zbl 1145.94402 |

[109] | 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. |

[110] | Q. McNemar. 1947. Note on the Sampling Error of the Difference between Correlated Proportions or Percentages. Psychometrika 12, 2 (1947), 153–157. |

[111] | 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. |

[112] | 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. |

[113] | 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. |

[114] | 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. |

[115] | 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. · Zbl 05970318 |

[116] | 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. |

[117] | 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. |

[118] | 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. · Zbl 05263684 |

[119] | 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 |

[120] | N. Oza. 2001. Online Ensemble Learning. Ph.D. Dissertation. University of California Berkeley. |

[121] | E. S. Page. 1954. Continuous Inspection Schemes. Biometrika 41, 1/2 (1954), 100–115. · Zbl 0056.38002 |

[122] | 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. |

[123] | 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. |

[124] | F. Rosenblatt. 1958. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review 65, 6 (1958), 386–408. |

[125] | 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. |

[126] | 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. |

[127] | M. Salganicoff. 1993. Density-Adaptive Learning and Forgetting. In Proc. of the Int. Conf. on Mach. Learn. (ICML). Morgan Kaufmann, 276–283. |

[128] | 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. |

[129] | J. Schlimmer and R. Granger. 1986. Incremental Learning from Noisy Data. Mach. Learn. 1, 3 (1986), 317–354. |

[130] | M. Scholz and R. Klinkenberg. 2007. Boosting Classifiers for Drifting Concepts. Intell. Data Anal. 11, 1 (2007), 3–28. |

[131] | 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. |

[132] | 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. |

[133] | A. Shiryaev. 2009. On Stochastic Models and Optimal Methods in the Quickest Detection Problems. Theory Probab. Appl. 53, 3 (2009), 385–401. · Zbl 1395.62249 |

[134] | 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. |

[135] | 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. |

[136] | A. Tartakovsky and G. Moustakides. 2010. State-of-the-Art in Bayesian Changepoint Detection. Sequential Anal. 29 (2010), 125–145. · Zbl 1190.62151 |

[137] | 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. |

[138] | A. Tsymbal. 2004. The Problem of Concept Drift: Definitions and Related Work. Tech. rep. Department of Computer Science, Trinity College, Dublin. |

[139] | 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. |

[140] | W. M. P. van der Aalst. 2011. Process Mining—Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin. I–XVI, 1–352 pages. · Zbl 1216.68016 |

[141] | W. M. P. van der Aalst. 2012. Process Mining. Commun. ACM 55, 8 (2012), 76–83. · Zbl 1357.68146 |

[142] | J. Vitter. 1985. Random Sampling with a Reservoir. ACM Trans. Math. Softw. 11, 1 (1985), 37–57. · Zbl 0562.68028 |

[143] | 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. |

[144] | V. Vovk. 1998. A Game of Prediction with Expert Advice. J. Comput. Syst. Sci. 56, 2 (1998), 153–173. · Zbl 0945.68528 |

[145] | A. Wald. 1947. Sequential Analysis. John Wiley and Sons. · Zbl 0029.15805 |

[146] | 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. |

[147] | G. Widmer. 1997. Tracking Context Changes through Meta-Learning. Mach. Learn. 27, 3 (June 1997), 259–286. |

[148] | 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. |

[149] | G. Widmer and M. Kubat. 1996. Learning in the Presence of Concept Drift and Hidden Contexts. Mach. Learn. 23, 1 (1996), 69–101. |

[150] | 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. · Zbl 05063424 |

[151] | 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. |

[152] | 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. |

[153] | 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. |

[154] | Z. Zhang and J. Zhou. 2010. Transfer Estimation of Evolving Class Priors in Data Stream Classification. Pattern Recogn. 43, 9 (2010), 3151–3161. · Zbl 1207.68334 |

[155] | 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. |

[156] | I. Zliobaite. 2009. Learning under Concept Drift: An Overview. Tech. rep. Vilnius University. |

[157] | I. Zliobaite. 2011a. Combining Similarity in Time and Space for Training Set Formation under Concept Drift. Intell. Data Anal. 15, 4 (2011), 589–611. |

[158] | I. Zliobaite. 2011b. Controlled Permutations for Testing Adaptive Classifiers. In Proc. of the 14th Int. Conf. on Discovery Science (DS). Springer, Berlin, 365–379. |

[159] | 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. |

[160] | 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. |

[161] | 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. |

[162] | 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. |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.