NONSTATIONARY STREAM DATA LEARNING WITH IMBALANCED CLASS DISTRIBUTION - Imbalanced Learning: Foundations, Algorithms, and Applications - page 180

REFERENCES
1. B. Babcock, S. Badu, M. Datar, R. Motwani, and J. Wisdom, “Models and issues in
data stream systems,” in Proceedings of PODS , (Madison, WI), ACM, 2002.
2. M. M. Gaber, S. Krishnaswamy, and A. Zaslavsky, “Adaptive mining techniques for
data streams using algorithm output granularity,” in Workshop (AusDM 2003), Held
in conjunction with the 2003 Congress on Evolutionary Computation (CEC 2003) ,
(Newport Beach, CA), Springer-Verlag, 2003.
3. C. Aggarwal, Data Streams: Models and Algorithms . Berlin: Springer Press,
2007.
4. A. Sharma, “A note on batch and incremental learnability,” Journal of Computer and
System Sciences , vol. 56, no. 3, pp. 272-276, 1998.
5. S. Lange and G. Grieser, “On the power of incremental learning,” Theoretical Com-
puter Science , vol. 288, no. 2, pp. 277-307, 2002.
6. M. D. Muhlbaier, A. Topalis, and R. Polikar, “Learn++.nc: Combining ensemble
of classifiers with dynamically weighted consult-and-vote for efficient incremental
learning of new classes,” IEEE Transactions on Neural Networks , vol. 20, no. 1, pp.
152-168, 2009.
7. P. Domingos and G. Hulten, “Mining high-speed data streams,” in Proceedings of
the International Conference KDD , (Boston, MA), pp. 71-80, ACM Press, 2000.
8. R. Polikar, L. Udpa, S. Udpa, and V. Honavar, “Learn++: An incremental learning
algorithm for supervised neural networks,” IEEE Transactions on System, Man and
Cybernetics (C), Special Issue on Knowledge Management , vol. 31, pp. 497-508,
2001.
9. H. He and S. Chen, “IMORL: Incremental multiple-object recognition and local-
ization,” IEEE Transactions on Neural Networks , vol. 19, no. 10, pp. 1727-1738,
2008.
10. Y. Freund and R. Schapire, “Decision-theoretic generalization of on-line learning and
application to boosting,” Journal of Computer and Systems Sciences , vol. 55, no. 1,
pp. 119-139, 1997.
11. P. Angelov and X. Zhou, “Evolving fuzzy systems from data streams in real-time,”
in IEEE Symposium on Evolving Fuzzy Systems (Ambelside, Lake District, UK), pp.
29-35, IEEE Press, 2006.
12. O. Georgieva and D. Filev, “Gustafson-Kessel algorithm for evolving data stream
clustering,” in Proc. Int. Conf. Computer Systems and Technologies for PhD Students
in Computing (Rousse, Bulgaria), ACM, Article 62, 2009.
13. D. Filev and O. Georgieva, “An extended version of the Gustafson-Kessel algorithm
for evolving data stream clustering,” in Evolving Intelligent Systems: Methodology
and Applications (P. Angelov, D. Filev, and N. Kasabov, eds.), pp. 273-300, John
Wiley and Sons, IEEE Press Series on Computational Intelligence, 2010.
14. S. Grossberg, “Nonlinear neural networks: Principles, mechanisms, and architectures,”
Neural Networks , vol. 1, no. 1, pp. 17-161, 1988.
15. W. N. Street and Y. Kim, “A streaming ensemble algorithm (SEA) for large-scale
classification,” in Proceedings of the Seventh ACM SIGKDD International Conference
Knowledge Discovery and Data Mining (San Francisco, CA, USA), pp. 377-382,
ACM Press, 2001.