10. References


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


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.


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.


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.


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


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.


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.


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.


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.


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


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.


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.


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.