A Multi-Level Stacking Ensemble Model Optimized by Soft Set Theory for Customer Churn Prediction

This study proposes a multi-level stacking ensemble model enhanced by Soft Set Theory to improve the accuracy and efficiency of customer churn prediction. The proposed model leverages Soft Set Theory to eliminate redundant classifiers via the analysis of the indiscernibility matrix, increasing classifier diversity and ensemble generalization. Ten base classifiers are considered at Level-1, from which five are selected: Gradient Boosting, Logistic Regression, XGBoost, Support Vector Machine, and CatBoost. Logistic Regression serves as the Level-2 meta-classifier. Experiments using the UCI Telco Churn dataset achieve an accuracy of 94.87% and an F1-score of 95.14%, while reducing computational time by over 50%. Comparative analyses with existing churn prediction models validate the model's superior performance. This framework demonstrates strong potential for implementation in telecommunications, healthcare, and finance sectors where customer retention is critical.