1 |
Altini, M. (2015). Dealing with imbalanced data: undersampling, oversampling and proper cross-validation. http://www.marcoaltini.com/blog/dealing-with-imbalanced-data-undersampling-oversampling-and-proper-cross-validation.
|
2 |
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence research, 16, 321-357.
DOI
|
3 |
Dal Pozzolo, A., Caelen, O., Waterschoot, S., and Bontempi, G. (2013). Racing for unbalanced methods selection. In International Conference on Intelligent Data Engineering and Automated Learning, (pp.24-31), Springer, Berlin, Heidelberg.
|
4 |
Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software, 33, 1-22.
|
5 |
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., and Herrera, F. (2012). A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42, 463-484.
DOI
|
6 |
He, H. and Garcia, E. A. (2009). Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, 21, 1263-1284.
DOI
|
7 |
He, H. and Ma, Y (2013). Imbalanced Learning: Foundations, Algorithms, and Applications, Wiley-IEEE Press, New Jersey.
|
8 |
Hulse, J. V., Khoshgoftaar, T. M., and Napolitano, A. (2007). Experimental perspectives on learning from imbalanced data. In Proceedings of the 24th International Conference on Machine Learning, 935-942.
|
9 |
Kuhn, M. (2016). Building predictive models in R using the caret package, Journal of Statistical Software, 28(5).
|
10 |
Liaw, A. and Wiener, M. (2002). Classification and regression by randomForest, R News, 2, 18-22.
|
11 |
Longadge, R. and Dongre, S. (2013). Class imbalance problem in data mining review, arXiv preprint arXiv:1305.1707
|
12 |
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2017). e1071: Misc Functions of the Department of Statistics, R package version 1.6-8.
|
13 |
Ren, P., Yao, S., Li, J., Valdes-Sosa, P. A., and Kendrick, K. M. (2015). Improved prediction of preterm delivery using empirical mode decomposition analysis of uterine electromyography signals, PLOS ONE, 10, e0132116
DOI
|
14 |
Ridgeway, G. (2017). gbm: generalized boosted regression models, R package version 2.1.3.
|
15 |
Xie, J. and Qiu, Z. (2007). The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition, 40, 557-562.
DOI
|