共 30 条
[1]
Chawla N.V., Japkowicz N., Editorial: Special issue on learning from imbalanced datasets, SIGKDD Explorations, 6, pp. 1-6, (2004)
[2]
Weiss G.M., Mining with rarity: A unifying framework, SIGKDD Explor Newsl, 6, pp. 7-19, (2004)
[3]
Japkowicz N., Holte R., Workshop report: AAAI-2000 workshop on learning from imbalanced data sets, AI Magazine, 22, pp. 127-136, (2001)
[4]
Crammer K., Singer Y., Cristianini N., Shawe-Taylor J., Williamson B., On the algorithmic implementation of multiclass kernel-based vector machines, Journal of Machine Learning Research, 2, (2001)
[5]
Wasikowski M., Chen X.W., Combating the small sample class imbalance problem using feature selection, Knowledge and Data Engineering IEEE Transactions on, 22, pp. 1388-1400, (2010)
[6]
Chen X.-W., Gerlach B., Casasent D., Pruning support vectors for imbalanced data classification, Proceedings of the International Joint Conference on Neural Networks, 3, pp. 1883-1888, (2005)
[7]
Tang Y., Zhang Y.Q., Chawla N., Krasser S., SVMs modeling for highly imbalanced classification, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 39, pp. 281-288, (2009)
[8]
Elkan C., Magical thinking in data mining: Lessons from coil challenge 2000, Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 426-431
[9]
Zhou Z.H., Liu X.Y., On multi-class cost-sensitive learning, Computational Intelligence, 26, pp. 232-257, (2010)
[10]
Bach F.R., Heckerman D., Horvitz E., Considering cost asymmetry in learning classifiers, J. Mach. Learn. Res, 7, pp. 1713-1741, (2006)