Research on Telecom Customer Churn Prediction Based on GA-XGBoost and SHAP

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ISSN Print: 2327-5219
ISSN Online: 2327-5227
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Citations

"Research on Telecom Customer Churn Prediction Based on GA-XGBoost and SHAP"
written by Ke Peng, Yan Peng,
published by Journal of Computer and Communications, Vol.10 No.11, 2022

has been cited by the following article(s):

[1]	Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods
	Engineering, Technology & Applied Science …, 2024

[2]	Explainability of Leverage Points Exploration for Customer Churn Prediction
	2023 IEEE International …, 2023

[3]	TELECOM CUSTOMER CHURN PREDICTION USING SUPERVISED MACHINE LEARNING TECHNIQUES
	Science and Technology, 2023

[4]	A Predictive Analysis of Customer Churn
	2023

[5]	Research on customer churn prediction and model interpretability analysis
	Plos one, 2023

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[2]	Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification Expert Systems, 2025 DOI:10.1111/exsy.70066

[3]	Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods Engineering, Technology & Applied Science Research, 2024 DOI:10.48084/etasr.7480

[4]	Explainability of Leverage Points Exploration for Customer Churn Prediction 2023 IEEE International Conference on Big Data (BigData), 2023 DOI:10.1109/BigData59044.2023.10386857

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