Zacytuj

Mena G., Coussement K., Bock K.W.D., Caigny A.D., Lessmann S., Exploiting time-varying RFM measures for customer churn prediction with deep neural networks, Annals of Operations Research, 1(23), 1-23, 2023. Search in Google Scholar

Amin A., Adnan A., Anwar S., An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes, Applied Soft Computing, 137(110103), 1-12, 2023. Search in Google Scholar

Shobana J., Gangadhar C., Arora R.K., Renjith P.N., Bamini J., Chincholkar Y.D., E-commerce customer churn prevention using machine learning-based business intelligence strategy, Measurement: Sensors, 27(100728), 1-8, 2023. Search in Google Scholar

Sharma A., Predictive Modelling (Customer Churn) Measurement and Prevention, Authorea, DOI:10.22541/au.167842113.31493988/v1, 2023. Search in Google Scholar

Tran H.D., Le N., Nguyen V.H., Customer churn prediction in the banking sector using machine learning-based classification models, Interdisciplinary Journal of Information Knowledge and Management, 18, 87-105, 2023. Search in Google Scholar

Lalwani P., Mishra M.K., Chadha J.S., Sethi P., Customer churn prediction system: a machine learning approach, Computing, 104(2), 271-294, 2022. Search in Google Scholar

Caigny A.D., Coussement K., Bock K.W.D., A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees, European Journal of Operational Research, 269(2), 760-772, 2018. Search in Google Scholar

Ahmad A.K., Jafar A., Aljoumaa K., Customer churn prediction in telecom using machine learning in big data platform, Journal of Big Data, 6(28), 1-24, 2019. Search in Google Scholar

Morozov V., Mezentseva O., Kolomiiets A., Proskurin M., Intellectual systems of decision making and problem of computational intelligence predicting customer churn using machine learning in IT startups, International Scientific Conference, 645-664, 2021. Search in Google Scholar

Dias J., Godinho P., Torres P., Machine learning for customer churn prediction in retail banking, Computational Science and Its Applications-ICCSA 2020, Springer, 2020. Search in Google Scholar

Agrawal S., Das A., Gaikwad A., Dhage S., Customer churn prediction modelling based on behavioural patterns analysis using deep learning, IEEE Access, 10, 14529-14547, 2022. Search in Google Scholar

Khodabandehlou S., Rahman M., Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior, Journal of Systems and Information Technology, 19(1/2), 65-93, 2017. Search in Google Scholar

Ali J., Khan R., Ahmad N., Maqsood I., Random forests and decision trees, International Journal of Computer Science Issues, 9(5), 272-278, 2012. Search in Google Scholar

Arafa A.A., Radad M., Badawy M., Fishawy N.E., Logistic regression hyperparameter optimization for cancer classification, Menoufia Journal of Electronic Engineering Research, 31(1), 1-8, 2022. Search in Google Scholar

Anand A., Pugalenthi G., Fogel G.B., Suganthan P.N., An approach for classification of highly imbalanced data using weighting and undersampling, Amino Acids, 39(5), 1385-1391, 2010. Search in Google Scholar

Zhu T., Lin Y., Liu Y., Synthetic minority oversampling technique for multiclass imbalance problems, Pattern Recognition, 72, 327-340, 2017. Search in Google Scholar

eISSN:
2956-7068
Język:
Angielski
Częstotliwość wydawania:
2 razy w roku
Dziedziny czasopisma:
Computer Sciences, other, Engineering, Introductions and Overviews, Mathematics, General Mathematics, Physics