Bio-Inspired Approach to Extend Customer Churn Prediction for the Telecom Industry in Efficient Way
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10697-6.pdf
Reference30 articles.
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2. Kim, S., Chang, Y., Wong, S. F., & Park, M. C. (2020). Customer resistance to churn in a mature mobile telecommunications market. International Journal of Mobile Communications, 18, 41–66.
3. Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2009). Customer churn prediction using improved balanced random forests. Expert Systems with Applications, 36, 5445–5449.
4. Fathian, M., Hoseinpoor, Y., & Minaei-Bidgoli, B. (2016). Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods. Kybernetes, 45, 732–743.
5. Holtrop, N., Wieringa, J. E., Gijsenberg, M. J., & Verhoef, P. C. (2017). No future without the past? Predicting churn in the face of customer privacy. International Journal of Research in Marketing, 34, 154–172.
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1. A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners;IEEE Access;2024
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