FOUNDATIONS OF IMBALANCED LEARNING - Imbalanced Learning: Foundations, Algorithms, and Applications - page 40

23. W. Wang, J. Yang, and P. Yu, “Efficient mining of weighted association rules (WAR),”
in Proceedings of the 6th ACM International Conference on Knowledge Discovery and
Data Mining (Boston, MA, USA), pp. 270 - 274, ACM, 2000.
24. H. Yao, H. Hamilton, and C. Butz, “A foundational approach to mining itemset
utilities from databases,” in Proceedings of the Fourth SIAM International Conference
on Data Mining (Lake Buena Vista, FL, USA), pp. 482 - 496, Society for Industrial
and Applied Mathematics (SIAM), 2004.
25. J. Yao and J. Hamilton, “Mining itemset utilities from transaction databases,” Data
and Knowledge Engineering , vol. 59, no. 3, pp. 603 - 626, 2006.
26. Y. Shen, Q. Yang, and Z. Zhang, “Objective-oriented utility-based association min-
ing,” in Proceedings of the 2002 IEEE International Conference on Data Mining
(Maebashi City, Japan), pp. 426 - 433, IEEE Computer Society, 2002.
27. G. Weiss and Y. Tian, “Maximizing classifier utility when there are data acquisi-
tion and modeling costs,” Data Mining and Knowledge Discovery , vol. 17, no. 2,
pp. 253 - 282, 2008.
28. D. Lewis and J. Catlett, “Heterogeneous uncertainty sampling for supervised learn-
ing,” in Proceedings of the Eleventh International Conference on Machine Learning
(New Brunswick, NJ, USA), pp. 148 - 156, Morgan Kaufmann, 1994.
29. S. Ertekin, J. Huang, and C. Giles, “Active learning for class imbalance problem,”
in Proceedings of the 30th International Conference on Research and Development in
Information Retrieval (Amsterdam, The Netherlands), ACM, 2007.
30. C. Ling and C. Li, “Data mining for direct marketing problems and solutions,” in
Proceedings of the Fourth International Conference on Knowledge Discovery and
Data Mining (New York, NY, USA), pp. 73 - 79, AAAI Press, 1998.
31. C. Drummond and R. Holte, “C4.5, class imbalance, and cost sensitivity: Why under-
sampling beats over-sampling,” in ICML Workshop on Learning from Imbalanced
Data Sets II, , (Washington, DC, USA), 2003.
32. N. Japkowicz and S. Stephen, “The class imbalance problem: A systematic study,”
Intelligent Data Analysis , vol. 6, no. 5, pp. 429 - 450, 2002.
33. N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, “SMOTE: Synthetic minor-
ity over-sampling technique,” Journal of Artificial Intelligence Research , vol. 16,
pp. 321 - 357, 2002.
34. M. Kubat and S. Matwin, “Addressing the curse of imbalanced training sets:
One-sided selection,” in Proceedings of the Fourteenth International Conference on
Machine Learning (Nashville, TN, USA), pp. 179 - 186, Morgan Kaufmann, 1997.
35. R. Yan, Y. Liu, R. Jin, and A. Hauptmann, “On predicting rare classes with SVM
ensembles in scene classification,” in Proceedings of IEEE International Conference
on Acoustics, Speech and Signal Processing (Hong Kong), IEEE Signal Processing
Society, 2003.
36. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning .
Reading, MA: Addison-Wesley, 1989.
37. A. Freitas, “Evolutionary computation,” in Handbook of Data Mining and Knowledge
Discovery (W. Klosgen and Jan M. Zytkow, eds.), pp. 698 - 706, New York, NY:
Oxford University Press, 2002.
38. G. Weiss, “Timeweaver: A genetic algorithm for identifying predictive patterns in
sequences of events,” in Proceedings of the Genetic and Evolutionary Computation
Conference (Orlando, FL, USA), pp. 718 - 725, Morgan Kaufmann, 1999.