Modeling concept drift: A probabilistic graphical model based approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9385, Issue , 2015, Pages 72-83

  Borchani, Hanen ^a   Martínez, Ana M ^a   Masegosa, Andrés R ^b   Langseth, Helge ^b   Nielsen, Thomas D ^a   Salmerón, Antonio ^c   Fernández, Antonio ^d   Madsen, Anders L ^a,e   Sáez, Ramón ^d  

^a AALBORG UNIVERSITY   (Denmark)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^c UNIVERSITY OF ALMERÍA   (Spain)

^d Banco de Crédito Cooperativo   (Spain)

^e HUGIN EXPERT A S   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

DATA HANDLING; GRAPHIC METHODS; INFORMATION ANALYSIS;

CLASSIFICATION ACCURACY; FINANCIAL DATA; LATENT VARIABLE; MODELING CONCEPTS; PROBABILISTIC GRAPHICAL MODELS; PROOF OF CONCEPT; SYNTHETIC DATASETS; VARIATIONAL BAYES;

SPEECH RECOGNITION;

EID: 84951985454     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-24465-5_7     Document Type: Conference Paper

References (17)

1. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer New York Inc., New York (2001)
2. Gaber, M.M., Zaslavsky, A.B., Krishnaswamy, S.: A survey of classification methods in data streams. In: Aggarwal, C.C. (ed.) Data Streams-Models and Algorithms. Advances in Database Systems, vol. 31, pp. 39–59. Springer, Berlin (2007)
3. Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Mach. Learn. 1, 317–354 (1986)
4. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)
5. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46, 44:1–44:37 (2014)
6. Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer, Berlin (2007)
7. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
8. Zhong, S., Langseth, H., Nielsen, T.D.: A classification-based approach to monitoring the safety of dynamic systems. Reliab. Eng. Syst. Safety 121, 61–71 (2014)
9. Bach, S., Maloof, M.: A Bayesian approach to concept drift. In: Advances in Neural Information Processing Systems, pp. 127–135 (2010)
10. Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Mach. Learn. 37, 183–233 (1999)
11. Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)
12. Martínez, M., Sucar, L.E.: Learning dynamic naive Bayesian classifiers. In: Proceedings of the Twenty-First International Florida Artificial Intelligence Research Symposium Conference, 655–659 (2008)
13. Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks. Artif. Intell. 42, 393–405 (1990)
14. Beal, M.J.: Variational algorithms for approximate Bayesian inference. Ph.D. thesis, Gatsby Computational Neuroscience Unit, University College London (2003)
15. Winn, J.M., Bishop, C.M.: Variational message passing. J. Mach. Learn. Res. 6, 661–694 (2005)
16. Street, N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 377–382 (2001)
17. Hulten, G., Spencer, L., Domingos, P.: Mining time changing data streams. In: Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining, pp. 97–106 (2001)