Dendritic Neural Regression Model Trained by Chicken Swarm Optimization Algorithm for Bank Customer Churn Prediction
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8184-7_20
Reference20 articles.
1. Zhao, X., et al.: Customer churn prediction based on feature clustering and nonparallel support vector machine. Int. J. Inf. Technol. Decis. Mak. 13(05): 1013–1027 (2014)
2. de Lima Lemos, R.A., Silva, T.C., Tabak, B.M.: Propension to customer churn in a financial institution: a machine learning approach. Neural Comput. Appl. 34(14): 11751–11768 (2022)
3. Alizadeh, M., et al.: Development of a customer churn model for banking industry based on hard and soft data fusion. IEEE Access 11, 29759–29768 (2023)
4. Xie, Y., et al.: Customer churn prediction using improved balanced random forests. Exp. Syst. Appl. 36(3), 5445–5449 (2009)
5. Coşer, A., et al.: Propensity to churn in banking: what makes customers close the relationship with a bank? Econ. Comput. Econ. Cybernet. Stud. Res. 54(2) (2020)
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