Publisher
Springer Berlin Heidelberg
Reference43 articles.
1. Amarasinghe, T., Aponso, A., Krishnarajah, N.: Critical analysis of machine learning based approaches for fraud detection in financial transactions. In: Proceedings of the 2018 International Conference on Machine Learning Technologies, pp. 12–17, ICMLT 2018. Association for Computing Machinery, New York, NY, USA (2018)
2. del Barrio, E., Gordaliza, P., Loubes, J.M.: Review of mathematical frameworks for fairness in machine learning (2020). https://doi.org/10.48550/ARXIV.2005.13755, https://arxiv.org/abs/2005.13755
3. Bellamy, R.K.E., et al.: AI fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. ArXiv abs/1810.01943 (2018)
4. Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: 2010 20th International Conference on Pattern Recognition, pp. 3121–3124. IEEE (2010)
5. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);NV Chawla,2003