Author:
Cao Peng,Zhao Dazhe,Zaïane Osmar R.
Publisher
Springer Berlin Heidelberg
Reference22 articles.
1. He, H., Garcia, E.: Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21, 1263–1284 (2009)
2. Yang, Q., Wu, X.: 10 challenging problems in data mining research. Int. J. Inf. Technol. Decis. Mak. 5(4), 597–604 (2006)
3. Chawla, N.V., Japkowicz, N., Kolcz, A.: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets 6(1), 1–6 (2004)
4. Li, N., Tsang, I., Zhou, Z.: Efficient Optimization of Performance Measures by Classifier Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence (99), 1 (2012)
5. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE Int. Conf. Neural Networks, pp. 1942–1948 (1995)
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