156
Views
0
CrossRef citations to date
0
Altmetric
Computer Science

An analysis on classification models for customer churn prediction

ORCID Icon, , , , , , & show all
Article: 2378877 | Received 26 Apr 2024, Accepted 06 Jul 2024, Published online: 17 Jul 2024

References

  • Ahmed, M., Afzal, H., Siddiqi, I., Amjad, M. F., & Khurshid, K. (2020). Exploring nested ensemble learners using overproduction and choose approach for churn prediction in telecom industry. Neural Computing and Applications, 32(8), 3237–3251. https://doi.org/10.1007/s00521-018-3678-8
  • Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242–254. https://doi.org/10.1016/j.neucom.2016.12.009
  • Athanasopoulos, G., Song, H., & Sun, J. A. (2017). Bagging in tourism demand modeling and forecasting. Journal of Travel Research, 57(1), 52–68. https://doi.org/10.1177/0047287516682871
  • Azeem, M., Usman, M., & Fong, A. (2017). A churn prediction model for prepaid customers in telecom using fuzzy classifiers. Telecommunication Systems, 66(4), 603–614. https://doi.org/10.1007/s11235-017-0310-7
  • Blouin, K. D., Flannigan, M. D., Wang, X., & Kochtubajda, B. (2016). Ensemble lightning prediction models for the province of Alberta, Canada. International Journal of Wildland Fire, 25(4), 421–432. https://doi.org/10.1071/WF15111
  • Brena, R. F., Zuvirie, E., Preciado, A., Valdiviezo, A., Gonzalez-Mendoza, M., & Zozaya-Gorostiza, C. (2021). Automated evaluation of foreign language speaking performance with machine learning. International Journal on Interactive Design and Manufacturing (IJIDeM), 15(2–3), 317–331. https://doi.org/10.1007/s12008-021-00759-z
  • Coussement, K., Lessmann, S., & Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decis Support Syst. http://linkinghub.elsevier.com/retrieve/pii/S0167923616302020
  • Dahiya, K., & Bhatia, S. (2015). Customer churn analysis in telecom industry. Reliability, Infocom Technologies and Optimization (ICRITO), (Trends and future directions), 1–6. https://doi.org/10.1109/ICRITO.2015.7359318
  • De Bock, K. W., & Van den Poel, D. (2011). An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. Expert Systems with Applications, 38(10), 12,293–12,301.
  • De, S., P, P., & Paulose, J. (2021 Effective ML techniques to predict customer churn [Paper presentation]. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 895–902), Coimbatore, India. https://doi.org/10.1109/ICIRCA51532.2021.9544785
  • Helini, K., Prathyusha, K., Sandhya Rani, K., & Raghavendran, C. V. (2020). Predicting coronary heart disease: A comparison between machine learning models. International Journal of Advanced Science and Technology, 29(3), 12635–12643. http://sersc.org/journals/index.php/IJAST/article/view/30385
  • Kisioglu, P., & Topcu, Y. I. (2011). Applying Bayesian belief network approach to customer churn analysis: A case study on the telecom industry of turkey. Expert Systems with Applications, 38(6), 7151–7157. https://doi.org/10.1016/j.eswa.2010.12.045
  • Liu, R., Ali, S., Bilal, S. F., Sakhawat, Z., Imran, A., Almuhaimeed, A., Alzahrani, A., & Sun, G. (2022). An intelligent hybrid scheme for customer churn prediction integrating clustering and classification algorithms. Applied Sciences, 12(18), 9355. https://doi.org/10.3390/app12189355
  • Lu, N., Lin, H., Lu, J., & Zhang, G. (2014). A customer churn prediction model in telecom industry using boosting. IEEE Transactions on Industrial Informatics, 10(2), 1659–1665. https://doi.org/10.1109/TII.2012.2224355
  • Mukhopadhyay, D., Malusare, A., Nandanwar, A., & Sakshi, S. (2021). An approach to mitigate the risk of customer churn using machine learning algorithms. In Joshi, A., Khosravy, M., & Gupta, N. (Eds.), Machine learning for predictive analysis. Lecture notes in networks and systems (Vol. 141). Springer. https://doi.org/10.1007/978-981-15-7106-0_13
  • Narsimha, B., Raghavendran, C. V., Rajyalakshmi, P., Reddy, G. K., Bhargavi, M., & Naresh, P. (2022). Cyber defense in the age of artificial intelligence and machine learning for financial fraud detection application. International Journal of Electrical and Electronics Research, 10(2), 87–92. https://doi.org/10.37391/ijeer.100206
  • Nishimatsu, K., & Inoue, A. (2023). User intent-based segmentation analysis for internet access services. International Journal of Strategic Decision Sciences, 14(1), 1–21. https://doi.org/10.4018/IJSDS.318643
  • Qureshi, S. A., Rehman, A. S., Qamar, A. M., Kamal, A., & Rehman, A. (2013). Telecommunication subscribers’ churn prediction model using machine learning. In 2013 8th International Conference on Digital Information Management (ICDIM) (pp. 131–136). IEEE.
  • Ravi, C., Raghavendran, C. V., Satish, G. N., Reddy, K. V., Reddy, G. K., & Balakrishna, C. (2023). ANN and RSM based modeling of Moringa Stenopetala seed oil extraction: Process optimization and oil characterization. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 329–338. https://doi.org/10.17762/ijritcc.v11i7s.7007
  • Sharma, A., Panigrahi, D., & Kumar, P. (2011). A neural network based approach for predicting customer churn in cellular network services. International Journal of Computer Applications, 27(11), 26–31. https://doi.org/10.5120/3344-4605
  • Sharma, V., Misra, J., & Singhal, S. (2023). Machine learning algorithms based advanced optimization of wire-EDM parameters: An experimental investigation into titanium alloy. International Journal on Interactive Design and Manufacturing (IJIDeM). https://doi.org/10.1007/s12008-023-01348-y
  • Sjarif, N. N., Rusydi, M., Yusof, M., Hooi, D., Wong, T., Ya’akob, S., Ibrahim, R., & Osman, M. Z. (2019). A customer churn prediction using Pearson correlation function and K nearest neighbor algorithm for telecommunication industry.
  • Sjarif, N., Azmi, N. F., Sarkan, H. M., Sam, S., & Osman, M. (2020). Predicting churn: How multilayer perceptron method can help with customer retention in telecom industry. IOP Conference Series: Materials Science and Engineering, 864(1), 012076. https://doi.org/10.1088/1757-899X/864/1/012076
  • Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547–12553. https://doi.org/10.1016/j.eswa.2009.05.032
  • Wang, Y., Feng, D., Li, D., Chen, X., Zhao, Y., & Niu, X. (2016). A mobile recommendation system based on logistic regression and gradient boosting decision trees [Paper presentation]. 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 1896–1902). IEEE. https://doi.org/10.1109/IJCNN.2016.7727431
  • Zhang, W., Zou, H., Luo, L., Liu, Q., Wu, W., & Xiao, W. (2016). Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing, 173, 979–987. https://doi.org/10.1016/j.neucom.2015.08.054
  • Zhao, L., Gao, Q., Dong, X., Dong, A., & Dong, X. (2017). K-local maximum margin feature extraction algorithm for churn prediction in telecom. Cluster Computing, 20(2), 1401–1409. https://doi.org/10.1007/s10586-017-0843-2