Customer churn prediction based on the integration of meta-learning network of the forest

To address the challenge of capturing temporal features in customer churn prediction tasks by tree models, a churn prediction method based on ensemble forest meta-learning network (EFML) was proposed. Firstly, data quality was improved through grouping strategies and class imbalance issues were addressed with undersampling techniques. Secondly, semantic vectors of user temporal features were constructed using EFML’s semantic graph constructor to depict fine-grained user behavior, forming a semantic graph and explicitly integrating it. Finally, multiple base tree models were trained as meta-learners, with the inputs being multilayer perceptron (MLP) to generate comprehensive churn prediction results. Experimental results demonstrate that EFML can effectively exploit differences in customer historical behaviors, capture and learn complementary relationships between base tree models. Compared to random forest (RF), EFML shows a 2.7% increase in AUC, a 3.7% increase in AP, and a significant improvement in prediction accuracy. This framework, combining tree models and micro-level features, possesses excellent interpretability, providing a new perspective for operators to achieve more refined user-centric management.