References

Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. 2002. “SMOTE: Synthetic Minority over-Sampling Technique.” Journal of Artificial Intelligence Research 16: 321–57.

Chen, Chao, Andy Liaw, and Leo Breiman. 2004. “Using Random Forest to Learn Imbalanced Data.” University of California, Berkeley.

Dal Pozzolo, Andrea, Olivier Caelen, Reid A Johnson, and Gianluca Bontempi. 2015. “Calibrating Probability with Undersampling for Unbalanced Classification.” In 2015 IEEE Symposium Series on Computational Intelligence, 159–66. IEEE.

Elkan, Charles. 2001. “The Foundations of Cost-Sensitive Learning.” In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 973–78. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.

Guo, Haixiang, Yijing Li, Jennifer Shang, Mingyun Gu, Yuanyue Huang, and Bing Gong. 2017. “Learning from Class-Imbalanced Data: Review of Methods and Applications.” Expert Systems with Applications 73: 220–39.

Han, Hui, Wen-Yuan Wang, and Bing-Huan Mao. 2005. “Borderline-SMOTE: A New over-Sampling Method in Imbalanced Data Sets Learning.” In International Conference on Intelligent Computing, 878–87. Springer.

Johnson, Justin M., and Taghi M. Khoshgoftaar. 2019. “Survey on Deep Learning with Class Imbalance.” Journal of Big Data 6 (1): 27.

Kearns, Michael, and Aaron Roth. 2019. The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Oxford University Press.

King, Gary, and Langche Zeng. 2001. “Logistic Regression in Rare Events Data.” Political Analysis 9 (2): 137–63.

Liu, Xu-Ying, Jianxin Wu, and Zhi-Hua Zhou. 2008. “Exploratory Undersampling for Class-Imbalance Learning.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39 (2): 539–50.

Noble, Safiya Umoja. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press.

O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.

Saltz, Jeffrey S., and Neil Dewar. 2019. “Data Science Ethical Considerations: A Systematic Literature Review and Proposed Project Framework.” Ethics and Information Technology 21 (3): 197–208.

Sun, Yanmin, Andrew KC Wong, and Mohamed S Kamel. 2007. “Cost-Sensitive Boosting for Classification of Imbalanced Data.” Pattern Recognition 40 (12): 3358–78.

VanderPlas, Jake. 2016. Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media, Inc.