10. References

BSanchezGR03

Ricardo Barandela, José Salvador Sánchez, V Garca, and Edgar Rangel. Strategies for learning in class imbalance problems. Pattern Recognition, 36(3):849–851, 2003.

BBM03

Gustavo EAPA Batista, Ana LC Bazzan, and Maria Carolina Monard. Balancing training data for automated annotation of keywords: a case study. In WOB, 10–18. 2003.

BPM04

Gustavo EAPA Batista, Ronaldo C Prati, and Maria Carolina Monard. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter, 6(1):20–29, 2004.

CBHK02

Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357, 2002.

CLB+04

Chao Chen, Andy Liaw, Leo Breiman, and others. Using random forest to learn imbalanced data. University of California, Berkeley, 110(1-12):24, 2004.

GarciaSanchezM12

Vicente García, José Salvador Sánchez, and Ramón Alberto Mollineda. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowledge-Based Systems, 25(1):13–21, 2012.

HWM05

Hui Han, Wen-Yuan Wang, and Bing-Huan Mao. Borderline-smote: a new over-sampling method in imbalanced data sets learning. In International conference on intelligent computing, 878–887. Springer, 2005.

HBGL08

Haibo He, Yang Bai, Edwardo A Garcia, and Shutao Li. Adasyn: adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 1322–1328. IEEE, 2008.

KM+97

Miroslav Kubat, Stan Matwin, and others. Addressing the curse of imbalanced training sets: one-sided selection. In Icml, volume 97, 179–186. Nashville, USA, 1997.

LDB17

Felix Last, Georgios Douzas, and Fernando Bacao. Oversampling for imbalanced learning based on k-means and smote. arXiv preprint arXiv:1711.00837, 2017.

LWZ08

Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(2):539–550, 2008.

NCK09

Hien M Nguyen, Eric W Cooper, and Katsuari Kamei. Borderline over-sampling for imbalanced data classification. In Proceedings: Fifth International Workshop on Computational Intelligence & Applications, volume 2009, 24–29. IEEE SMC Hiroshima Chapter, 2009.

SKVHN09

Chris Seiffert, Taghi M Khoshgoftaar, Jason Van Hulse, and Amri Napolitano. Rusboost: a hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(1):185–197, 2009.