Classifier Ensembles for Changing Environments

1. Aha, D., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)

   Google Scholar 

2. Bezdek, J.C., Kuncheva, L.I.: Nearest prototype classifier designs: An experimental study. International Journal of Intelligent Systems 16(12), 1445–1473 (2001)

   Article  MATH  Google Scholar 

3. Blum, A.: Empirical support for Winnow and weighted-majority based algorithms: results on a calendar scheduling domain. In: Proc. 12th International Conference on Machine Learning, pp. 64–72. Morgan Kaufmann, San Francisco (1995)

   Google Scholar 

4. Breiman, L.: Pasting small votes for classification in large databases and on-line. Machine Learning 36, 85–103 (1999)

   Article  Google Scholar 

5. Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1990)

   Google Scholar 

6. Domingos, P., Hulten, G.: Mining high-speed data streams. In: Knowledge Discovery and Data Mining, pp. 71–80 (2000)

   Google Scholar 

7. Domingos, P., Hulten, G.: A general framework for mining massive data streams. Journal of Computational and Graphical Statistics 12, 945–949 (2003)

   Article  MathSciNet  Google Scholar 

8. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)

   Article  MATH  MathSciNet  Google Scholar 

9. Ganti, V., Gehrke, J., Ramakrishnan, R.: Mining data streams under block evolution. ACM SIGKDD Explorations Newsletter 3, 1–10 (2002)

   Article  Google Scholar 

10. Hart, P.E.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory 16, 515–516 (1968)

    Article  Google Scholar 

11. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proc. 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106. ACM Press, New York (2001)

    Chapter  Google Scholar 

12. Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation 3, 79–87 (1991)

    Article  Google Scholar 

13. Kelly, M.G., Hand, D.J., Adams, N.M.: The impact of changing populations on classifier performance. In: Proc. 5th ACM SIGDD International Conference on Knowledge Discovery and Data Mining, pp. 367–371. ACM Press, San Diego (1999)

    Chapter  Google Scholar 

14. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, New York (2000)

    Google Scholar 

15. Lim, C.P., Harrison, R.E.: Online pattern classification with multiple neural network systems: An experimental study. IEEE Transactions on Systems, Man, and Cybernetics 35, 235–247 (2003)

    Google Scholar 

16. Littlestone, N.: Learning quickly when irrelevant attributes abound: A new linear threshold algorithm. Machine Learning 2, 285–318 (1988)

    Google Scholar 

17. Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Inform. Computation 108, 212–261 (1994)

    Article  MATH  MathSciNet  Google Scholar 

18. Markou, M., Singh, S.: Novelty detection: a review. Part 1: statistical approaches. Signal Processing 83(12), 2481–2497 (2003)

    Article  MATH  Google Scholar 

19. Oza, N.C.: Online Ensemble Learning. PhD thesis, University of California, Berkeley (2001)

    Google Scholar 

20. Salganicoff, M.: Density-adaptive learning and forgetting. In: Proceedings of the 10th International Conference on Machine Learning, pp. 276–283 (1993)

    Google Scholar 

21. Street, W.N., Kim, Y.S.: A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377–382. ACM Press, New York (2001)

    Chapter  Google Scholar 

22. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept drifting data streams using ensemble classifiers. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 226–235. ACM Press, New York (2003)

    Chapter  Google Scholar 

23. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69–101 (1996)

    Google Scholar 

24. Widmer, G., Kubat, M.: Special Issue on Context Sensitivity and Concept Drift. Machine Learning 32 (1998)

    Google Scholar 

Download references