C. C. Aggarwal, Outlier Analysis, 2013.

A. C. Bahnsen, S. Villegas, D. Aouada, B. Ottersten, A. M. Correa et al., Fraud detection by stacking cost-sensitive decision trees, 2017.

A. Beygelzimer, E. Hazan, S. Kale, and H. Luo, Online gradient boosting, Advances in Neural Information Processing Systems (NIPS), pp.2458-2466, 2015.

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, 1984.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.

A. Buja, W. Stuetzle, and Y. Shen, Loss functions for binary class probability estimation and classification: Structure and applications, 2005.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, Smote: Synthetic minority over-sampling technique, J. Artif. Int. Res, vol.16, issue.1, pp.321-357, 2002.

C. Elkan, The foundations of cost-sensitive learning, Proceedings of the 17th International Joint Conference on Artificial Intelligence, vol.2, pp.973-978

, IJCAI'01, 2001.

Y. Freund and R. E. Schapire, A short introduction to boosting, Proceedings of the Sixteenth IJCAI, pp.1401-1406, 1999.

J. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting, Annals of Statistics, vol.28, 1998.

J. H. Friedman, Greedy function approximation: A gradient boosting machine, Annals of Statistics, vol.29, pp.1189-1232, 2000.

P. Li, C. J. Burges, and Q. Wu, Mcrank: Learning to rank using multiple classification and gradient boosting, Proceedings of the 20th International Conference on Neural Information Processing Systems, p.7, 2007.

C. Ling, V. Sheng, and Q. Yang, Test strategies for cost-sensitive decision trees, IEEE Transactions on Knowledge and Data Engineering, vol.18, issue.8, pp.1055-1067, 2006.

N. Nikolaou, N. Edakunni, M. Kull, P. Flach, and G. Brown, Cost-sensitive boosting algorithms: Do we really need them?, Machine Learning, vol.104, issue.2, pp.359-384, 2016.

S. Puthiya-parambath, N. Usunier, and Y. Grandvalet, Optimizing f-measures by cost-sensitive classification, NIPS, pp.2123-2131, 2014.

Y. Sahin, S. Bulkan, and E. Duman, A cost-sensitive decision tree approach for fraud detection, Expert Syst. Appl, vol.40, issue.15, pp.5916-5923, 2013.

Y. Sun, M. S. Kamel, A. K. Wong, and Y. Wang, Cost-sensitive boosting for classification of imbalanced data, Pattern Recognition, vol.40, issue.12, pp.3358-3378, 2007.

N. Thai-nghe, Z. Gantner, and L. Schmidt-thieme, Cost-sensitive learning methods for imbalanced data, The 2010 International Joint Conference on Neural Networks (IJCNN), 2010.