Kimura, T. Customer churn prediction with hybrid resampling and ensemble learning. J. Manag. Inform. Decis. Sci. 25(1), 123 (2022).

MathSciNet Google Scholar

Lalwani, P., Mishra, M.K., Chadha, J.S. and Sethi, P. Customer churn prediction system: a machine learning approach.Computing, pp.124 (2022).

Hadden, J., Tiwari, A., Roy, R. & Ruta, D. Computer assisted customer churn management: State-of- the-art and future trends. Comput. Oper. Res. 34(10), 29022917 (2007).

Article Google Scholar

Rajamohamed, R. & Manokaran, J. Improved credit card churn prediction based on rough clustering and supervised learning techniques. Clust. Comput. 21(1), 6577 (2018).

Article Google Scholar

Backiel, A., Baesens, B. & Claeskens, G. Predicting time-to-churn of prepaid mobile telephone customers using social network analysis. J. Operat. Res. Soc. 67(9), 11351145. https://doi.org/10.1057/jors.2016.8 (2016).

Article Google Scholar

Zhu, B., Baesens, B. & Vanden Broucke, S. K. An empirical comparison of techniques for the class imbalance problem in churn prediction. Inform. Sci. 408, 8499. https://doi.org/10.1016/j.ins.2017.04.015 (2017).

Article Google Scholar

Vijaya, J. & Sivasankar, E. Computing efficient features using rough set theory combined with ensemble classification techniques to improve the customer churn prediction in telecommunication sector. Computing 100(8), 839860 (2018).

Article Google Scholar

Ahmad, S. N. & Laroche, M. S. Analyzing electronic word of mouth: A social commerce construct. Int. J. Inform. Manag. 37(3), 202213 (2017).

Article Google Scholar

Gaurav Gupta, S. A critical examination of different models for customer churn prediction using data mining. Int. J. Eng. Adv. Technol. 6(63), 850854 (2019).

Google Scholar

Abbasimehr, H., Setak, M. & Tarokh, M. A neuro-fuzzy classifier for customer churn prediction. Int. J. Comput. Appl. 19(8), 3541 (2011).

Google Scholar

Kumar, S. & Kumar, M. Predicting customer churn using artificial neural network. In Engineering Applications of Neural Networks: 20th International Conference, EANN 2019, Xersonisos, Crete, Greece, May 24-26, 2019, Proceedings (eds Macintyre, J. et al.) 299306 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-030-20257-6_25.

Chapter Google Scholar

Sharma, T., Gupta, P., Nigam, V. & Goel, M. Customer churn prediction in telecommunications using gradient boosted trees. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2019 Vol. 2 (eds Khanna, A. et al.) 235246 (Springer Singapore, 2020). https://doi.org/10.1007/978-981-15-0324-5_20.

Chapter Google Scholar

Umayaparvathi, V. & Iyakutti, K. A survey on customer churn prediction in telecom industry: Datasets, methods and metrics. Int. Res. J. Eng. Technol. 4(4), 10651070 (2016).

Google Scholar

Ahmad, A. K., Jafar, A. & Aljoumaa, K. Customer churn prediction in telecom using machine learning in big data platform. J. Big Data 6(1), 28 (2019).

Article Google Scholar

Extracted from: https://www.kaggle.com/competitions/customer-churn-prediction-2020/data?select=test.csv

Mishra, A. & Reddy, U. S. A comparative study of customer churn prediction in telecom industry using ensemble based classifiers. In 2017 International Conference on Inventive Computing and Informatics (ICICI). IEEE, 721725. (2017)

Coussement, K., Lessmann, S. & Verstraeten, G. A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decis. Support Syst. 95, 2736 (2017).

Article Google Scholar

Wang, Q. F., Xu, M. & Hussain, A. Large-scale ensemble model for customer churn prediction in search ads. Cogn. Comput. 11(2), 262270 (2019).

Article Google Scholar

Hashmi, N., Butt, N. A. & Iqbal, M. Customer churn prediction in telecommunication a decade review and classification. Int. J. Comput. Sci. Issues 10(5), 271 (2013).

Google Scholar

Eria, K. & Marikannan, B. P. Systematic review of customer churn prediction in the telecom sector. J. Appl. Technol. Innovat. 2(1), 714 (2018).

Google Scholar

Brnduoiu, I., Toderean, G. & Beleiu, H. Methods for churn prediction in the pre-paid mobile telecommunications industry. In 2016 International conference on communications (COMM), 97100. IEEE. (2016)

Singh, M., Singh, S., Seen, N., Kaushal, S., & Kumar, H. Comparison of learning techniques for prediction of customer churn in telecommunication. In 2018 28th International Telecommunication Networks and Applications Conference (ITNAC) IEEE, pp. 15. (2018)

Lee, E. B., Kim, J. & Lee, S. G. Predicting customer churn in the mobile industry using data mining technology. Ind. Manag. Data Syst. 117(1), 90109 (2017).

Article Google Scholar

Bharadwaj, S., Anil, B. S., Pahargarh, A., Pahargarh, A., Gowra, P. S., & Kumar, S. Customer Churn Prediction in Mobile Networks using Logistic Regression and Multilayer Perceptron (MLP). In 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), IEEE. pp. 436438, (2018)

Chen, T. & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785794. (2016)

Dhaliwal, S. S., Nahid, A. A. & Abbas, R. Effective intrusion detection system using XGBoost. Information 9(7), 149 (2018).

Article Google Scholar

Baesens, B., Hppner, S. & Verdonck, T. Data engineering for fraud detection. Decis. Support Syst. 150, 113492 (2021).

Article Google Scholar

Zhou, H., Chai, H. F. & Qiu, M. L. Fraud detection within bankcard enrollment on mobile device based payment using machine learning. Front. Inform. Technol. Electron. Eng. 19(12), 15371545 (2018).

Article Google Scholar

Pamina, J., Raja, B., SathyaBama, S. & Sruthi, M. S. An effective classifier for predicting churn in telecommunication. J. Adv. Res. Dyn. Control Syst. 11, 221229 (2019).

Google Scholar

Kuhn, M. & Johnson, K. Applied Predictive Modeling 26th edn. (Springer, 2013).

Book Google Scholar

Yijing, L., Haixiang, G., Xiao, L., Yanan, L. & Jinling, L. Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data. Knowl. -Based Syst. 94, 88104 (2016).

Article Google Scholar

Verbeke, W., Martens, D., Mues, C. & Baesens, B. Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst. Appl. 38(3), 23542364 (2011).

Article Google Scholar

Burez, J. & Van den Poel, D. Handling class imbalance in customer churn prediction. Expert Syst. Appl. 36(3), 46264636 (2009).

Article Google Scholar

Lpez, V., Fernndez, A., Garca, S., Palade, V. & Herrera, F. An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Inform. Sci. 250, 113141 (2013).

Article Google Scholar

Kaur, H., Pannu, H. S. & Malhi, A. K. A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Comput. Surv. (CSUR) 52(4), 136 (2019).

Google Scholar

Salunkhe, U. R. & Mali, S. N. A hybrid approach for class imbalance problem in customer churn prediction: A novel extension to under-sampling. Int. J. Intell. Syst. Appl. 11(5), 7181 (2018).

Google Scholar

Galar, M., Fernandez, A., Barrenechea, E., Bustince, H. & Herrera, F. A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C 42(4), 463484. https://doi.org/10.1109/TSMCC.2011.2161285 (2012).

Article Google Scholar

Singh, A. & Purohit, A. A survey on methods for solving data imbalance problem for classification. Int. J. Comput. Appl. 127(15), 3741 (2015).

Google Scholar

Schaefer, G., Krawczyk, B., Celebi, M. E. & Iyatomi, H. An ensemble classification approach for melanoma diagnosis. Memetic Comput. 6(4), 233240 (2014).

Article Google Scholar

Salunkhe, U. R. & Mali, S. N. Classifier ensemble design for imbalanced data classification: A hybrid approach. Proc. Comput. Sci. 85, 725732 (2016).

Article Google Scholar

Liu, X. Y., Wu, J. & Zhou, Z. H. Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B Cybern. 39(2), 539550 (2008).

Google Scholar

Haixiang, G., Yijing, L., Shang, J. & Mingyun, G. Learning from class-imbalanced data: Review of methods and applications. Expert Syst. Appl. 73, 220239 (2017).

Article Google Scholar

Douzas, G., Bacao, F. & Last, F. Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inform. Sci. 465, 120. https://doi.org/10.1016/j.ins.2018.06.056 (2018).

Article Google Scholar

Mahesh, B. Machine learning algorithms-a review. Int. J. Sci. Res. 9, 381386 (2020).

Google Scholar

Bonaccorso, G. Machine Learning Algorithms (Packt Publishing Ltd., 2017).

Google Scholar

Ray, S. A quick review of machine learning algorithms. In2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon). IEEE. pp. 3539, (2019)

Singh, A., Thakur, N. and Sharma, A., A review of supervised machine learning algorithms. In2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 13101315. 2016

Ayodele, T. O. Types of machine learning algorithms. New Adv. Mach. Learn. 3, 1948 (2010).

Google Scholar

Sagi, O. & Rokach, L. Ensemble learning: A survey. Wiley Interdisciplin. Rev.: Data Min. Knowled. Discov. 8(4), e1249 (2018).

Google Scholar

Zhang, C. & Ma, Y. (eds) Ensemble Machine Learning: Methods and Applications (Springer Science & Business Media, 2012).

Google Scholar

Amin, A., Adnan, A. & Anwar, S. An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Nave Bayes. Appl. Soft Comput. 137, 110103 (2023).

Article Google Scholar

Amin, A. et al. Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing 237, 242254 (2017).

Article Google Scholar

Amin, A., Shah, B., Khattak, A. M., Baker, T., & Anwar, S. Just-in-time customer churn prediction: With and without data transformation. In2018 IEEE congress on evolutionary computation (CEC), IEEE, pp. 16. (2018).

Amin, A., Shah, B., Abbas, A., Anwar, S., Alfandi, O., & Moreira, F. Features weight estimation using a genetic algorithm for customer churn prediction in the telecom sector. InNew Knowledge in Information Systems and Technologies: Vol. 2. Springer International Publishing. pp. 483491, (2019)

Chaubey, G. et al. Customer purchasing behavior prediction using machine learning classification techniques. J. Ambient Intell. Hum. Comput. https://doi.org/10.1007/s12652-022-03837-6 (2022).

Article Google Scholar

Thomas, W. E., & David, O. M. Chapter 4exploratory study.Research methods for cyber security, Syngress, 95130 (2017).

View post:

Enhancing customer retention in telecom industry with machine learning driven churn prediction | Scientific Reports - Nature.com