Stacking Ensemble Approach for Churn Prediction: Integrating CNN and Machine Learning Models with CatBoost Meta-Learner

Stacking Ensemble Approach for Churn Prediction: Integrating CNN and Machine Learning Models with CatBoost Meta-Learner

In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors that exhibit nonlinear dependencies. In these compl...

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Bibliographic Details
Main Authors: Tan Yan Lin, Pang Ying Han, Ooi Shih Yin, Khoh Wee How, Hiew Fu San
Format: Article
Language:English
Published: MMU Press 2023-09-01
Series:Journal of Engineering Technology and Applied Physics
Subjects:
customer churn prediction
heterogeneous ensemble learning
machine learning
deep learning
stacking ensemble
Online Access:https://journals.mmupress.com/index.php/jetap/article/view/624/411

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