Just-in-time classifiers for recurrent concepts

IEEE Transactions on Neural Networks and Learning Systems

Volumn 24, Issue 4, 2013, Pages 620-634

  Alippi, Cesare ^a   Boracchi, Giacomo ^a   Roveri, Manuel ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Adaptive classifiers; concept drift; just in time classifiers; recurrent concepts

Indexed keywords

ADAPTIVE CLASSIFIERS; CHANGE DETECTION; CONCEPT DRIFTS; JUST IN TIME; NON-STATIONARY CONDITION; RECURRENT CONCEPTS; STATIONARY CONDITIONS;

ARTIFICIAL INTELLIGENCE; COMPUTER NETWORKS;

JUST IN TIME PRODUCTION;

EID: 84875892348     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2239309     Document Type: Article

References (41)

1. J. B. Gomes, E. Menasalvas, and P. A. C. Sousa, "Learning recurring concepts from data streams with a context-aware ensemble," in Proc. ACM Symp. Appl. Comput., 2011, pp. 994-999.
2. C. Alippi and M. Roveri, "Just-in-time adaptive classifiers-part I: Detecting nonstationary changes," IEEE Trans. Neural Netw., vol. 19, no. 7, pp. 1145-1153, Jul. 2008.
3. C. Alippi and M. Roveri, "Just-in-time adaptive classifiers-part II: Designing the classifier," IEEE Trans. Neural Netw., vol. 19, no. 12, pp. 2053-2064, Dec. 2008.
4. C. Alippi, G. Boracchi, and M. Roveri, "A Just-in-time adaptive classification system based on the Intersection of Confidence Intervals rule," Neural Netw., vol. 24, no. 8, pp. 791-800, 2011.
5. C. Alippi, G. Boracchi, and M. Roveri, "Just-in-time ensemble of classifiers," in Proc. Int. Joint Conf. Neural Netw., Jun. 2012, pp. 1-8.
6. I. Zliobaite, "Learning under concept drift: An overview," (2010). [Online]. Available: http://arxiv.org/abs/1010.4784
7. R. Elwell and R. Polikar, "Incremental learning of concept drift in nonstationary environments," IEEE Trans. Neural Netw., vol. 22, no. 10, pp. 1517-1531, Oct. 2011.
8. L. L. Minku, A. P. White, and X. Yao, "The impact of diversity on online ensemble learning in the presence of concept drift," IEEE Trans. Knowl. Data Eng., vol. 22, no. 5, pp. 730-742, May 2010.
9. C. Alippi, G. Boracchi, and M. Roveri, "A distributed self-adaptive nonparametric change-detection test for sensor/actuator networks," in Artificial Neural Networks and Machine Learning (Lecture Notes in Computer Science), vol. 6792, T. Honkela, W. Duch, M. Girolami, and S. Kaski, Eds. Berlin, Germany: Springer-Verlag, 2011, pp. 173-180.
10. C. Alippi, G. Boracchi, and M. Roveri, "A hierarchical, nonparametric, sequential change-detection test," in Proc. Int. Joint Conf. Neural Netw., Aug. 2011, pp. 2889-2896.
11. C. Alippi, G. Boracchi, and M. Roveri, "An effective just-in-time adaptive classifier for gradual concept drifts," in Proc. Int. Joint Conf. Neural Netw., Aug. 2011, pp. 1675-1682.
12. C. Alippi, G. Boracchi, and M. Roveri, "Adaptive classifiers with ICIbased adaptive knowledge base management," in Proc. 20th INt. Conf. Artif. Neural Netw., vol. 6353. 2010, pp. 458-467.
13. C. Alippi, G. Boracchi, and M. Roveri, "Change detection tests using the ICI rule," in Proc. Int. Joint Conf. Neural Netw., 2010, pp. 1-7.
14. C. Alippi, G. Boracchi, and M. Roveri, "Just in time classifiers: Managing the slow drift case," in Proc. Int. Joint Conf. Neural Netw., 2009, pp. 114-120.
15. J. Gama, P. Medas, G. Castillo, and P. Rodrigues, "Learning with drift detection," in Advances in Artificial Intelligence (Lecture Notes in Computer Science), vol. 3171. Berlin, Germany: Springer-Verlag, 2004, pp. 66-112.
16. K. Nishida and K. Yamauchi, "Learning, detecting, understanding, and predicting concept changes," in Proc. Int. Joint Conf. Neural Netw., 2009, pp. 2280-2287.
17. M. Baena-García, J. D. Campo-Àvila, R. Fidalgo, and A. Bifet, "Early drift detection method," Proc. 4th ECML PKDD Int. Workshop Knowl. Dis. Data Streams, Sep. 2006, pp. 77-86.
18. K. Nishida and K. Yamauchi, "Detecting concept drift using statistical testing," in Discovery Science. New York, USA: Springer-Verlag, 2007, pp. 264-269.
19. J. Patist, "Optimal window change detection," in Proc. 7th IEEE Int. Conf. Data Mining Workshops 2007, pp. 557-562.
20. A. Bifet and R. Gavalda, "Learning from time-changing data with adaptive windowing," in Proc. SIAM Int. Conf. Data Mining, 2007, pp. 443-448.
21. L. Cohen, G. Avrahami-Bakish, M. Last, A. Kandel, and O. Kipersztok, "Real-time data mining of non-stationary data streams from sensor networks," Inf. Fusion, vol. 9, no. 3, pp. 344-353, 2008. (Pubitemid 351680781)
22. 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)
23. Y. Yang, X.Wu, and X. Zhu, "Mining in anticipation for concept change: Proactive-reactive prediction in data streams," Data Min. Knowl. Discov., vol. 13, no. 3, pp. 261-289, Nov. 2006. (Pubitemid 44455021)
24. M. Lazarescu, "A multi-resolution learning approach to tracking concept drift and recurrent concepts," in Proc. PRIS, 2005, p. 52.
25. S. Ramamurthy and R. Bhatnagar, "Tracking recurrent concept drift in streaming data using ensemble classifiers," in Proc. 6th Int. Conf. Machine Learn. Appl., Dec. 2007, pp. 404-409.
26. I. Katakis, G. Tsoumakas, and I. Vlahavas, "Tracking recurring contexts using ensemble classifiers: An application to email filtering," Knowl. Inf. Syst., vol. 22, no. 3, pp. 371-391, Mar. 2010.
27. G. Widmer, "Tracking context changes through meta-learning," Mach. Learn., vol. 27, no. 3, pp. 259-286, Jun. 1997. (Pubitemid 127510031)
28. J. a. B. Gomes, E. Menasalvas, and P. A. C. Sousa, "Tracking recurrent concepts using context," in Proc. 7th Int. Conf. Rough Sets Current Trends Comput., 2010, pp. 168-177.
29. J. a. B. Gomes, M. M. Gaber, P. A. C. Sousa, and E. Menasalvas, "Context-aware collaborative data stream mining in ubiquitous devices," in Proc. 10th Int. Conf. Adv. Intell. Data Anal., 2011, pp. 22-33.
30. J. a. B. Gomes, E. Menasalvas, and P. A. C. Sousa, "Improving the learning of recurring concepts through high-level fuzzy contexts," in Proc. 5th Int. Conf. Rough Set Knowl. Technol., 2010, pp. 234-239.
31. J. Gama and P. Kosina, "Tracking recurring concepts with metalearners," in Progress in Artificial Intelligence (Lecture Notes in Computer Science), vol. 5816, L. Lopes, N. Lau, P. Mariano, and L. Rocha, Eds. Berlin, Germany: Springer-Verlag, 2009, pp. 423-434.
32. J. Gama and P. Kosina, "Learning about the learning process," in Proc. 10th Int. Conf. Adv. Intell. Data Anal., 2011, pp. 162-172.
33. 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.
34. M. Basseville and I. V. Nikiforov, Detection of Abrupt Changes: Theory and Application. Upper Saddle River, NJ, USA: Prentice-Hall, 1993.
35. G. S. Mudholkar and M. C. Trivedi, "A Gaussian approximation to the distribution of the sample variance for nonnormal populations," J. Amer. Stat. Assoc., vol. 76, no. 374, pp. pp. 479-485, 1981.
36. A. Goldenshluger and A. Nemirovski, "On spatial adaptive estimation of nonparametric regression," Math. Meth. Stat., vol. 6, no. 2, pp. 135-170, 1997. (Pubitemid 127116989)
37. V. Katkovnik, "A new method for varying adaptive bandwidth selection," IEEE Trans. Signal Process., vol. 47, no. 9, pp. 2567-2571, Sep. 1999.
38. R. Johnson and D. Wichern, Applied Multivariate Statistical Analysis. Englewood Cliffs, NJ, USA: Prentice-Hall, 2002.
39. J. C. Hsu, J. T. G. Hwang, H.-K. Liu, and S. J. Ruberg, "Confidence intervals associated with tests for bioequivalence," Biometrika, vol. 81, no. 3, pp. 103-114, 1994.
40. P. Bauer and M. Kieser, "A unifying approach for confidence intervals and testing of equivalence and difference," Biometrika, vol. 83, no. 4, pp. 934-937, 1996.
41. W. N. Street and Y. Kim, "A streaming ensemble algorithm (SEA) for large-scale classification," in Proc. 7th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2001, pp. 377-382.