Countering the concept-drift problem in big data using iOVFDT

Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013

Volumn , Issue , 2013, Pages 126-132

  Yang, Hang ^a   Fong, Simon ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

classification; concept drift; data stream mining; incremental decision tree

Indexed keywords

CONCEPT DRIFTS; DATA ANALYTICS; DATA STREAM MINING; DECISION SUPPORT APPLICATIONS; DECISION TREE LEARNING; DECISION-TREE ALGORITHM; PREDICATE LOGIC; STATISTICAL PROPERTIES;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA COMMUNICATION SYSTEMS; DECISION TREES;

DATA MINING;

EID: 84886036088     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BigData.Congress.2013.25     Document Type: Conference Paper

References (24)

1. A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer, "MOA: Massive Online Analysis," Journal of Machine Learning Research, 11, pp. 1601-1604, 2010.
2. P. Domingos and G. Hulten, "Mining high-speed data streams," in Proc. of the 6th ACM SIGKDD, pp. 71-80, 2000.
3. A. Bifet and R. Gavalda""Learning from time-changing data with adaptive windowing," in Proc. of the SIAM International Conference on Data Mining, pp. 443-448, 2007.
4. H.Yang and S.Fong, "Incrementally optimized decision tree for noisy big data," in Proc. of the BigMine Workshop of the 18thACM SIGKDD, pp. 36-44, 2012.
5. G. Hulten, L. Spencer, and P. Domingos, "Mining time-changing data streams," in Proc. of the 7th ACM SIGKDD, pp. 97-106, 2001.
6. J. Gama, R. Rocha and P. Medas, "Accurate decision trees for mining high-speed data streams," in Proc. of the 9th ACM SIGKDD, pp. 523-528, 2003.
7. C. Jean, "Assessing agreement on classification tasks: The kappa statistic." Computational Linguistics, 22(2), pp. 249-254, 1996.
8. B. Kevin, Pratt and G. Tschapek, "Visualizing concept drift," in Proc. of the 9th ACM SIGKDD. pp. 735-740, 2003.
9. A. Tsymbal, The problem of concept drift: definitions and related work. 2004.
10. G. Widmer and M. Kubat, "Learning in the presence of concept drift and hidden contexts," MachineLearning, 23 (1), pp.69-101, 1996.
11. M. Kubat and G. Widmer, "Adapting to drift in continuous domains," Tech. Report ÖFAI-TR-94-27, Austrian Research Institute for Artificial Intelligence, Vienna, 1994.
12. M. Salganicoff, "Tolerating concept and sampling shift in lazy learning using prediction error context switching," AI Review, Special Issue on Lazy Learning, 11 (1-5), pp.133-155, 1997.
13. R. Klinkenberg, "Learning drifting concepts: Example selection vs. example weighting." Intell. Data Anal, 8 (3), pp. 281-300, 2004.
14. G. Widmer and M. Kubat, "Effective learning in dynamic environments by explicit context tracking," Proc. of the 6th European Conf. on Machine Learning, Springer-Verlag, Lecture Notes in Computer Science 667, 1993, 227-243.
15. H. Sattar, and Y. Ying, "Flexible decision tree for data stream classi-cation in the presence of concept change, noise and missing values," Data Min. Knowl. Discov, 19 (1), pp. 95-131, 2009.
16. B. Pfahringer, G. Holmes, and R. Kirkby, "New options for Hoeffding trees," in Proc. of the 20th Australian joint conference on advances in artfficial intelligence, pp. 90-99, 2007.
17. A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, R. Gavalda, "New ensemble methods for evolving data streams," in Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, pp. 139-147, 2009.
18. N. Oza, and S. Russell, "Online bagging and boosting," In Artificial Intelligence and Statistics, Morgan Kaufmann, pp. 105-112, 2001.
19. J. Gama, P. Medas, and R. Rocha, "Forest trees for on-line data," in Proc. of the 2004 ACM symposium on Applied Computing. ACM, New York, pp.632-636, 2004.
20. A. Bifet and E. Frank. "Sentiment knowledge discovery in twitterstreaming data." Discovery Science, pp. 1-15, 2010.
21. H. Yang, S. Fong, Incremental Optimization Mechanism for Constructing A Decision Tree in Data Stream Mining, International Journal of Mathematical Problems in Engineering, Hindawi press, 2013, Ready for publishing. DOI:10.1155/2013/580397
22. J. Gama, P. Medas, G. Castillo, and P. Rodrigues. "Learning with drift detection," in A. Bazzan and S. Labidi, editors, Advances in Artificial Intelligence SBIA 2004, Volume 3171 of Lecture Notes in Computer Science, pp. 66-112. Springer Berlin/Heidelberg, 2004.
23. M. Baena-Garcia, J. Del Campo-Avila, R. Fidalgo, A. Bifet, R. Gavalda, and R. Morales-Bueno, "Early drift detection methods," in International Workshop on Knowledge Discovery from Data Streams, pp.77-86, 2006.
24. S. H. Bach and M. A. Maloof, "Paired learners for concept drift," in IEEE International Conference on Data Mining, pp.23-32, IEEE Computer Society, 2008.