Author:
Sharma Shweta,Gosain Anjana,Jain Shreya
Reference25 articles.
1. Amin, A., et al. (2016). Comparing oversampling technique to handle the CIP: A customer churn prediction case study. IEEE Access, 4, 7940–7957.
2. Ali, A., & Shamsuddin, S. M. (2015). Classification with class imbalance problem. International Journal of Advances in Soft Computing and its Applications, 7(3).
3. Vimalraj, S., & Porkodi, R. (2018). A review on handling imbalanced data. In Proceedings 2018 IEEE International Conference on Current Trends towards Converging Technologies.
4. Barua, S., Islam, M. M., & Murase, K. (2013). ProWSyn: Proximity weighted synthetic oversampling technique for imbalanced data set learning. In Advances in knowledge discovery and data mining (pp. 317–328). Heidelberg: Springer.
5. Haixiang, G., Li, Y., Shang, J., Mingyun, G., Yuanyue, H., & Gong, B. (2016). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications, 73. https://doi.org/10.1016/j.eswa.2016.12.035.
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