<![CDATA[Customer churn prediction remains a critical challenge in the telecommunications industry because subscriber attrition directly affects revenue generation, customer retention, and long-term business sustainability. Although numerous studies have employed machine learning and deep learning techniques for churn prediction, many existing approaches rely on standalone models that have limited ability to simultaneously capture complex nonlinear customer behaviour, exploit model diversity, and eliminate redundant features. Building upon our previous study, this research proposes an enhanced heterogeneous stacking ensemble framework that integrates Binary Firefly Algorithm (BFA)-based feature optimization with Deep Neural Network (DNN), Support Vector Machine (SVM), and Random Forest (RF) as base learners, while Logistic Regression serves as the meta-learner for final churn classification. To ensure a fair and controlled comparison, the same Maven Analytics Telecom Customer Churn dataset and preprocessing strategy adopted in the previous study were retained, including data cleaning, feature transformation, stratified data partitioning, and normalization. Model development further incorporated 5-fold cross-validation on the training dataset, while BFA was introduced to identify the most informative pre-churn features. Experimental results demonstrate that the proposed framework achieved strong and balanced predictive performance and demonstrated improvements in several classification metrics compared with the previously developed BFA-based Hybrid TabNet-DNN model. Furthermore, its performance was competitive with the Random Forest baseline, which exhibited comparable classification effectiveness. These findings show that optimized feature selection and heterogeneous ensemble learning improve prediction stability and generalization. The proposed framework provides an effective decision-support tool for proactive customer retention in the telecommunications industry.]]>
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