Multi-Classifier Evidence Ensemble Algorithm-Based for Predicting Travelers Repurchases of China's Airlines

Repurchase prediction is a vital aspect of marketing strategy and a complex decision-making task, especially in the airline industry, where data are uncertain, incomplete, and ambiguous. To address this, this study proposes a novel multi-classifier evidence ensemble algorithm that integrates evidence theory with machine learning to predict travelers' repurchase behavior. The model was trained using 29 behavioral features derived from a low-cost Chinese airline. Empirical results show that the proposed algorithm outperforms traditional models in terms of the accuracy, the precision, the recall, the F1-score, and the AUC. Specifically, it achieved over 80% accuracy and precision in binary classification tasks. Ablation experiments using four classifier combinations at different sampling rates (30%, 50%, and 70%) further validated the robustness and effectiveness of the framework. The results suggest that the proposed ensemble framework outperforms traditional prediction models in terms of overall predictive performance for analyzing airline passenger behavior in real-world settings.