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Cited by 11
The Impact of Churn Labelling Rules on Churn Prediction in Telecommunications

2024 IEEE XXXI International Conference on Electronics, Electrical Engineering and Computing (INTERCON)
Luis Tejada-Vicente, Dana Rosado-Oliden, David Mauricio-Santos
https://doi.org/10.1109/INTERCON63140.2024.10833498
Conference  (2024), p. 1
2025 15th International Conference on Advanced Computer Information Technologies (ACIT)
Ana Perišić, Ivana Beljo
https://doi.org/10.1109/ACIT65614.2025.11185641
Conference  (2025), p. 9
A Deep Auto Imputation Integrated Bayes Optimized Transfer Learning Model with Hybrid Skill-Levy Search Algorithm (DAI-BOTS) for Call Drop Prediction in Mobile Networks
G. V. Ashok, P. Vasanthi Kumari
https://doi.org/10.12720/jait.15.8.941-955
Journal  Journal of Advances in Information Technology Volume 15, Issue 8 (2024), p. 941
A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners
Awais Manzoor, M. Atif Qureshi, Etain Kidney, Luca Longo
https://doi.org/10.1109/ACCESS.2024.3402092
Journal  IEEE Access Volume 12 (2024), p. 70434
Binary Firefly Algorithm-Based Optimization of TabNet and DNN Models for Customer Churn Prediction
Philip O. Odion, Maaruf M. Lawal, Sonia S. Joshua, Abdulrashid Abdulrauf
https://doi.org/10.62411/faith.3048-3719-376
Journal  Journal of Future Artificial Intelligence and Technologies Volume 3, Issue 2 (2026), p. 257
Customer Segmentation Using RFM and Big Data: A Bibliometric Analysis (2017–2024)
Mauricio Santamaria-Ruiz, Moisés García-Jiménez, Alexander Troncoso-Palacio, Claudia Torres-Linares, Carlos Cortegana-Diaz
https://doi.org/10.1007/978-3-032-34434-2_15
Book  Lecture Notes in Networks and Systems (Proceedings of 11th International Congress on Information and Communication Technology) Volume 2120 (2027), p. 168
Factors, Predictability, and Explainability of Mobile Telephony Customer Departure in Telecommunications Companies: A Systematic Review of the Literature
David Freire, David Santos Mauricio Sanchez, José Luis Castillo Sequera, Daniel Fiallo Moncayo
https://doi.org/10.1109/ACCESS.2024.3443318
Journal  IEEE Access Volume 12 (2024), p. 118968
Leveraging Machine Learning And Deep Learning Models for Proactive Churn Customer Retention
Hira Farman, Samar Raza Talpur, Usman Amjad, Govari Shankar, Umm e Laila, Lubaba Naseem
https://doi.org/10.21015/vtse.v12i4.1928
Journal  VFAST Transactions on Software Engineering Volume 12, Issue 4 (2024), p. 70
Machine Learning-Based Prediction Models to Predict Customer Churn: A Systematic Review
Estelo Honorio Diego Alessandro, Santiago Quispe Frider Vagner, Giancarlo Sanchez Atuncar
https://doi.org/10.1007/978-3-032-22091-2_18
Book  Lecture Notes in Networks and Systems (Focus on Artificial Intelligence in Intelligent Systems Design) Volume 1902 (2026), p. 236
Realistic Data Delays and Alternative Inactivity Definitions in Telecom Churn: Investigating Concept Drift Using a Sliding-Window Approach
Andrej Bugajev, Rima Kriauzienė, Viktoras Chadyšas
https://doi.org/10.3390/app15031599
Journal  Applied Sciences Volume 15, Issue 3 (2025), p. 1599
Sample Denoising and Optimization Technique Based on Noise Filtering and Evolutionary Algorithms for Imbalanced Data Classification
Fhira Nhita, Asniar, Isman Kurniawan, Adiwijaya
https://doi.org/10.1109/ACCESS.2025.3573786
Journal  IEEE Access Volume 13 (2025), p. 99314


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