, 2008 This chapter covers different approaches that may be taken when building an ensemble method, through studying specific examples of each approach from research conducted by the authors.
Eastwood, Mark, Gabrys, Bogdan
core
Research on telecom industry customer churn prediction based on explainable machine learning models
Dianxin kexueIn the telecom industry, accurate prediction of customer churn is crucial for the companies involved to maintain market competitiveness and increase revenue.
WANG Shengjie, ZHANG Qinghong
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A Dynamic Classification Approach to Churn Prediction in Banking Industry
, 2020 Churn prediction is the process of using transaction data to identify customers who are likely to cease their relationship with a company. To date, most work in churn prediction focuses on sampling strategies and supervised modeling over a short period ...
Chung, Wingyan, Leung, Hoiyin Christina
core
A new perspective on data leakage prevention and adaptive attention in telecom churn prediction. [PDF]
Sci RepLe DT, Nguyen MT.
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A novel hybrid deep learning framework for customer churn prediction using RFM and embedding clustering. [PDF]
Sci RepIbrahim S +3 more
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Neural network approach enhancing churn prediction with categorical encoding and standard scaling. [PDF]
Sci RepBhattacharjee B +7 more
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Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory. [PDF]
Entropy (Basel), 2020 Kostić SM, Simić MI, Kostić MV.
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Improving bank customer churn prediction with feature reduction using GA. [PDF]
Sci RepT N N, Pramod D.
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Explainable AI-driven customer churn prediction: a multi-model ensemble approach with SHAP-based feature analysis. [PDF]
Front Artif IntellEl Attar A, El-Hajj M.
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