Active learning with drifting streaming data

IEEE Transactions on Neural Networks and Learning Systems

Volumn 25, Issue 1, 2014, Pages 27-39

  Zliobaite, Indre ^a   Bifet, Albert ^b   Pfahringer, Bernhard ^c   Holmes, Geoffrey ^c  

^a BOURNEMOUTH UNIVERSITY   (United Kingdom)

^b YAHOO RESEARCH   (Spain)

^c UNIVERSITY OF WAIKATO   (New Zealand)

Author keywords

Active learning; concept drift; data streams; user feedback

Indexed keywords

ACTIVE LEARNING; ACTIVE LEARNING STRATEGIES; CONCEPT DRIFTS; DATA STREAM; DECISION BOUNDARY; DYNAMIC ALLOCATIONS; PREDICTIVE MODELING; USER FEEDBACK;

LEARNING SYSTEMS;

DATA COMMUNICATION SYSTEMS;

EID: 84891134709     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2012.2236570     Document Type: Article

References (25)

1. D. Cohn, L. Atlas, and R. Ladner, "Improving generalization with active learning," Mach. Learn., vol. 15, no. 2, pp. 201-221, May 1994.
2. G. Widmer and M. Kubat, "Learning in the presence of concept drift and hidden contexts," Mach. Learn., vol. 23, no. 1, pp. 69-101, Apr. 1996. (Pubitemid 126737384)
3. I. Zliobaite, A. Bifet, B. Pfahringer, and G. Holmes, "Active learning with evolving streaming data," in Proc. 2011 Eur. Conf. Mach. Learn. Knowl. Discovery Databases, Sep. 2011, pp. 597-612.
4. J. Attenberg and F. Provost, "Online active inference and learning," in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Jul. 2011, pp. 186-194.
5. R. Klinkenberg, "Using labeled and unlabeled data to learn drifting concepts," in Proc. Workshop Notes IJCAI'01 Learn. Temporal Spatial Data, Aug. 2001, pp. 16-24.
6. D.Widyantoro and J. Yen, "Relevant data expansion for learning concept drift from sparsely labeled data," IEEE Trans. Knowl. Data Eng., vol. 17, no. 3, pp. 401-412, Mar. 2005. (Pubitemid 40320171)
7. M. Masud, C. Woolam, J. Gao, L. Khan, J. Han, K. Hamlen, and N. Oza, "Facing the reality of data stream classification: Coping with scarcity of labeled data," Knowl. Inf. Syst., vol. 33, no. 1, pp. 213-244, Oct. 2011.
8. S. Huang and Y. Dong, "An active learning system for mining timechanging data streams," Intell. Data Anal., vol. 11, no. 4, pp. 401-419, Sep. 2007.
9. P. Lindstrom, S. J. Delany, and B. M. Namee, "Handling concept drift in a text data stream constrained by high labelling cost," in Proc. 23rd Florida Artif. Intell. Res. Soc. Conf., May 2010, pp. 1-7.
10. X. Zhu, P. Zhang, X. Lin, and Y. Shi, "Active learning from data streams," in Proc. 7th IEEE Int. Conf. Data Mining, Oct. 2007, pp. 757-762.
11. W. Fan, Y. Huang, H. Wang, and P. Yu, "Active mining of data streams," in Proc. 4th SIAM Int. Conf. Data Mining, Apr. 2004, pp. 457-461.
12. P. Lindstrom, B. M. Namee, and S. J. Delany, "Drift detection using uncertainty distribution divergence," in Proc. IEEE 11th Int. Conf. Data Mining Workshops, Dec. 2011, pp. 604-608.
13. H. Borchani, P. Larranaga, and C. Bielza, "Mining concept-drifting data streams containing labeled and unlabeled instances," in Proc. 23rd Int. Conf. Ind. Eng. Appl. Appl. Intell. Syst. Trends Appl. Intell. Syst., Jun. 2010, pp. 531-540.
14. P. Zhang, X. Zhu, and L. Guo, "Mining data streams with labeled and unlabeled training examples," in Proc. 9th IEEE Int. Conf. Data Mining, Dec. 2009, pp. 627-636.
15. P. Li, X. Wu, and X. Hu, "Mining recurring concept drifts with limited labeled streaming data," ACM Trans. Intell. Syst. Technol., vol. 3, no. 2, pp. 29-1-29-32, Feb. 2012.
16. N. Cesa-Bianchi, C. Gentile, and L. Zaniboni, "Worst-case analysis of selective sampling for linear classification," J. Mach. Learn. Res., vol. 7, no. 7, pp. 1205-1230, 2006. (Pubitemid 44015299)
17. J. Gama, P. Medas, G. Castillo, and P. Rodrigues, "Learning with drift detection," in Proc. 17th Brazilian Symp. Artif. Intell. Adv. Artif. Intell., Sep. 2004, pp. 286-295. (Pubitemid 39751638)
18. B. Settles, Active Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning). San Francisco: Morgan Claypool, 2012.
19. Y. Yuan, "A review of trust region algorithms for optimization," in Proc. 4th Int. Congr. Ind. Appl. Math., Jun. 1999, pp. 271-282.
20. A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer, "MOA: Massive online analysis," J. Mach. Learn. Res., vol. 11, pp. 1601-1604, Jun. 2010.
21. M. Harries, C. Sammut, and K. Horn, "Extracting hidden context," Mach. Learn., vol. 32, no. 2, pp. 101-126, 1998. (Pubitemid 40626077)
22. A. Frank and A. Asuncion. (2010). UCI machine learning repository [Online]. Available: http://archive.ics.uci.edu/ml
23. E. Ikonomovska, J. Gama, and S. Dzeroski, "Learning model trees from evolving data streams," Data Mining Knowl. Discovery, vol. 23, no. 1, pp. 128-168, Jul. 2011.
24. D. Lewis, Y. Yang, T. Rose, and F. Li, "RCV1: A new benchmark collection for text categorization research," J. Mach. Learn. Res., vol. 5, pp. 361-397, Apr. 2004.
25. A. McCallum and K. Nigam, "A comparison of event models for naive bayes text classification," in Proc. AAAI/ICML'98 Workshop Learn. Text Categorizat., Jul. 1998, pp.41-48.