Domain adaptation bounds for multiple expert systems under concept drift

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2014, Pages 595-601

  Ditzler, Gregory ^a   Rosen, Gail ^a   Polikar, Robi ^b  

^a Drexel University   (United States)

^b Rowan University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STREAMS; EXPERT SYSTEMS; FORECASTING; PROBABILITY DISTRIBUTIONS;

ANALYTICAL RESULTS; CLASSIFICATION MODELS; DIFFERENT DOMAINS; ENSEMBLE APPROACHES; MULTIPLE CLASSIFIERS; NON-STATIONARY ENVIRONMENT; PREDICTION ALGORITHMS; VERIFICATION LATENCIES;

LEARNING SYSTEMS;

EID: 84908475950     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2014.6889909     Document Type: Conference Paper

References (33)

1. M. Sugiyama and M. Kawanabe, "Machine learning in nonstationary environments." The MIT Press, 2012.
2. J. Gama, I. Žliobaitě, A. Bifet, M. Pechenizkiy, and A. Bouchachia, "A survey on concept drift adaptation", ACM Computing Surveys, 2013.
3. L. I. Kuncheva, "Classifier ensembles for changing environments", in Proceedings 5th International Workshop on Multiple Classifier Systems, pp. 1-15, 2004.
4. L. L. Minku and X. Yao, "DDD: A new ensemble approach for dealing with concept drift", IEEE Transactions on Knowledge Discovery and Data Engineering, vol. 24, no. 4, pp. 619-633, 2012.
5. J. Gao, W. Fan, J. Han, and P. S. Yu, "A general framework for mining concept-drifting data streams with skewed distributions", in Proceedings of the 7th SIAM International Conference on Data Mining, pp. 203-208, 2007.
6. J. Gao, B. Ding, W. Fan, J. Han, and P. S. Yu, "Classifying data streams with skewed class distributions and concept drifts", IEEE Internet Computing, vol. 12, no. 6, pp. 37-49, 2008.
7. S. Grossberg, "Nonlinear neural networks: Principles, mechanisms, and architectures", Neural Networks, vol. 1, no. 1, pp. 17-61, 1988.
8. B. Settles, "Active learning literature survey", Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2010.
9. R. Elwell and R. Polikar, "Incremental learning of concept drift in nonstationary environments", IEEE Transactions on Neural Networks, vol. 22, no. 10, pp. 1517-1531, 2011.
10. G. Ditzler and R. Polikar, "Incremental learning of concept drift from streaming imbalanced data", IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 10, pp. 2283-2301, 2013.
11. A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, and R. Gavalda, "New ensemble methods for evolving data streams", in Knowledge and Data Discovery, 2009.
12. G. Ditzler and R. Polikar, "Hellinger distance based drift detection for nonstationary environments", in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 41-48, 2011.
13. C. Alippi and M. Roveri, "Change detection tests using the ICI rule", in International Joint Conference on Neural Networks, pp. 1190-1196, 2010.
14. J. Kolter and M. Maloof, "Dynamic weighted majority: An ensemble method for drifting concepts", Journal of Machine Learning Research, vol. 8, pp. 2755-2790, 2007.
15. V. Vapnik, The Nature of Statistical Learning Theory. Springer-Verilag, 2nd ed., 1999.
16. S. Geman, E. Bienenstock, and R. Doursat, "Neural networks and the bias/variance dilemma", Neural Computation, vol. 4, pp. 1-58, 1992.
17. G. Brown, J. Wyatt, and P. Tiňo, "Managing diversity in regression ensembles", Journal of Machine Learning Research, vol. 6, pp. 1621-1650, 2005.
18. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons, Inc., 2004.
19. G. Brown, Encyclopedia of Machine Learning, ch. Ensemble Learning. Springer Press, 2010.
20. G. Ditzler, G. Rosen, and R. Polikar, "Discounted expert weighting for concept drift", in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 61-67, 2013.
21. W. N. Street and Y. Kim, "A streaming ensemble algorithm (SEA) for large scale classification", in Proceedings to the 7th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 377-382, 2001.
22. D. Brzezinski and J. Stefanowski, "From block-based ensembles to online learners in changing data streams: If- and how-to", in ECML PKDD, Workshop on Instant Interactive Data Mining, 2012.
23. S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman Vaughan, "A theory of learning from different domains", Machine Learning, vol. 79, pp. 151-175, 2010.
24. I. Žliobaitě, "Expected classification error of the euclidean linear classifier under sudden concept drift", in International Conference on Fuzzy Systems and Knowledge Discovery, vol. 2, pp. 29-33, 2008.
25. D. Kifer, S. Ben-David, and J. Gehrke, "Detecting change in data streams", in Proceedings of the 30th VLDB Conference, pp. 180-191, 2004.
26. G. Ditzler, G. Rosen, and R. Polikar, "A transductive learning algorithm for nonstationary environments", in International Joint Conference on Neural Networks, pp. 945-952, 2012.
27. G. Ditzler and R. Polikar, "Semi-supervised learning in nonstationary environments", in International Joint Conference on Neural Networks, pp. 2471-2478, 2011.
28. P. Ruvolo and E. Eaton, "ELLA: An efficient lifelong learning algorithm", in International Conference on Machine Learning, 2013.
29. P. Ruvolo and E. Eaton, "Scalable lifelong learning with active task selection", in AAAI Conference on Artificial Intelligence, 2013.
30. R. Elwell and R. Polikar, "Incremental learning in nonstationary environments with controlled forgetting", in International Joint Conference on Neural Networks, pp. 771-778, 2009.
31. I. Žliobaitě, "Change with Delayed Labeling: when is it detectable?", in IEEE International Conference on Data Mining Workshops, 2010.
32. M. Harries, "Splice-2 comparative evaluation: Electricity pricing", tech. rep., The University of South Wales, 1999.
33. S. Delany, P. Cunningham, and A. Tsymbal, "A comparison of ensemble and case-base maintenance techniques for handling concept drift in spam filtering", in International Conference on Artificial Intelligence, pp. 340-345, 2006.