Accès libre

Multi-Level Machine Learning Model to Improve the Effectiveness of Predicting Customers Churn Banks

 et   
19 sept. 2024
À propos de cet article

Citez
Télécharger la couverture

Agarap, A. F. Deep Learning Using Rectified Linear Units (RELU). – ArXiv, abs/1803.08375, 2018. Search in Google Scholar

Banerjee, C., T. Mukherjee, E. L. Pasiliao. An Empirical Study on Generalizations of the RELU Activation Function. – In: Proc. of ACM Southeast Conference, 2019. Search in Google Scholar

Gustavo, E. A., P. A. Batista, R. C. Prati, M. C. Monard. A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. – SIGKDD Explor., Vol. 200, 2004, No 6, pp. 20-29. Search in Google Scholar

Breiman, L. Stacked Regressions. – Machine Learning, Vol. 24, 2004, pp. 49-64. Search in Google Scholar

Hemlata Dalmia, C. V., S. S. Nikil, S. Kumar. Churning of Bank Customers Using Supervised Learning. – In: Informations on Elektronics and Communications Engeneering, 2020, pp. 681-691. Search in Google Scholar

Renato, A. L. L., T. C. Silva, B. M. Tabak. Propension to Customer Churn in a Financial Institution: A Machine Learning Approach. – Neural Computing & Applications, Vol. 34, 2022, pp. 11751-11768. Search in Google Scholar

Domingos, E., B. Ojeme, O. J. Daramola. Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector. – Comput., Vol. 9, 2021, No 34. Search in Google Scholar

Hassonah, M. A., A. Rodan, A.-K. Al-Tamimi, J. Alsakran. Churn Prediction: A Comparative Study Using KNN and Decision Trees. – In: Proc. of 6th HCT Information Technology Trends (ITT’19), 2019, pp. 182-186. Search in Google Scholar

He, Benlan, Y. Shi, Q. Wan, X. Zhao. Prediction of Customer Attrition of Commercial Banks Based on SVM Model. – Procedia Computer Science, Vol. 31, 2014, pp. 423-430. Search in Google Scholar

Bing, Q. H., M. T. Kechadi, B. Buckley, G. Kiernan, E. J. Keogh, T. A. Rashid. A New Feature Set with New Window Techniques for Customer Churn Prediction in Land-Line Telecommunications. – Expert Syst. Appl. Vol. 37, 2010, pp. 3657-3665. Search in Google Scholar

Kaur, I., J. Kaur. Customer Churn Analysis and Prediction in Banking Industry Using Machine Learning. – In: Proc. of 6th International Conference on Parallel, Distributed and Grid Computing (PDGC’20), 2020, pp. 434-437. Search in Google Scholar

Kaushik, H., D. Singh, M. Kaur, H. A. Alshazly, A. Zaguia, H. Hamam. Diabetic Retinopathy Diagnosis from Fundus Images Using Stacked Generalization of Deep Models. – IEEE Access, Vol. 9, 2021, pp. 108276-108292. Search in Google Scholar

Kumar, A. S., D. Chandrakala. An Optimal Churn Prediction Model Using Support Vector Machine with Adaboost. – Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, Vol. 2, 2017, No 1, 225-230. Search in Google Scholar

LeCun, Y., Y. Bengio, G. E. Hinton. Deep Learning. – Nature, Vol. 521, 2015, No 7553, pp. 436-444. Search in Google Scholar

Massaoudi, M., S. S. Refaat, I. Chihi, M. A. Trabelsi, F. S. Oueslati, H. Abu-Rub. A Novel Stacked Generalization Ensemble-Based Hybrid Lgbm-Xgb-Mlp Model for Short-Term Load Forecasting. – Energy, Vol. 214, 2021, No 3. Search in Google Scholar

Müller, A., S. Guido. Introduction to Machine Learning with Python: A Guide for Data Scientists. 2016. Search in Google Scholar

Noda, K., Y. Yamaguchi, K. Nakadai, H. G. Okuno, T. Ogata. Audio-Visual Speech Recognition Using Deep Learning. – Applied Intelligence, Vol. 42, 2014, pp. 722-737. Search in Google Scholar

Ravi, V., S. Bapi, R. Churn, C.-F. Tsai, Y.-H. Lu, W. Verbeke, D. Martens, C. Mues, B. Baesens, N. Lu, H. Lin, J. Lu, G. Zhang, B. He, Y. Shi, Q. Wan, X. Zhao, K. W. De Bock, D. Van den Poel, H. Lee, Y. Lee, H. S. Cho. A Survey on Customer Churn Prediction Using Machine Learning Techniques. – International Journal of Computer Applications, Vol. 154, 2016, pp. 13-16. Search in Google Scholar

Russakovsky, O., J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, L. Fei-Fei. Imagenet Large Scale Visual Recognition Challenge. – International Journal of Computer Vision, Vol. 115, 2014, pp. 211-252. Search in Google Scholar

Simonyan, K., A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. – CoRR, abs/1409.1556, 2014. Search in Google Scholar

Simonyan, K., A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. – CoRR, abs/1409.1556, 2015. Search in Google Scholar

Smyth, P., D. H. Wolpert. Stacked Density Estimation. – In: Neural Information Processing Systems Research Gate, 1997. Search in Google Scholar

Sunkaraneni, T. Bank Turnover Dataset. Online, August 2022. Search in Google Scholar

Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. Going Deeper with Convolutions. – In: Proc. of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’14), 2014, pp. 1-9. Search in Google Scholar

Ting, K. M., I. H. Witten. Issues in Stacked Generalization. – J. Artif. Intell. Res., Vol. 10, 1999, pp. 271-289. Search in Google Scholar

Ting, K. M., I. H. Witten. Issues in Stacked Generalization. – ArXiv, abs/1105.5466, 2011. Search in Google Scholar

Tolles, J., W. J. Meurer. Logistic Regression: Relating Patient Characteristics to Outcomes. – JAMA, Vol. 316, 2016, No 5, pp. 533-534. Search in Google Scholar

Hoang, D. T., N. T. Le, V.-H. Nguyen. Customer Churnprediction in the Banking Sector Using Machine Learning-Based Classification Models. – Interdisciplinary Journal of Information, Knowledge, and Management, 2023. Search in Google Scholar

Veningston, K., P. V. Rao, C. T. Selvan, M. Ronalda. Investigation on Customer Churn Prediction Using Machine Learning Techniques. – In: Proc. of International Conference on Data Science and Applications, 2021. Search in Google Scholar

Xu, T., Y. Ma, K. R. Kim. Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping. – Applied Science, Vol. 11, 2021. Search in Google Scholar

Zhang, X., J. J. Zhao, Y. LeCun. Character-Level Convolutional Networks for Text Classification. – In: Advances in Neural Information Processing Systems (NIPS 2015), Vol. 28, 2015. Search in Google Scholar

Tu, C. Exploratory Analysis of Bank Customer Attrition. Kaggle, 2020. Exploratory Analysis of Bank Customer Attrition. Accessed July 2024. Search in Google Scholar

Galal, M., S. Rady, M. Aref. Enhancing Customer Churn Prediction in Digital Banking Using Ensemble Modeling. – In: Proc. of 4th IEEE Novel Intelligent and Leading Emerging Sciences Conference (NILES’22), 2022, pp. 21-25. Search in Google Scholar

Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Informatique, Informatique