An Improved Prediction Algorithm for Coupon Usage Based on XGBoost Multi-dimensional Classification

Addressing the limitations of traditional binary classification models in scenarios with ambiguous user intentions, this paper proposes a hybrid classification framework (TWD-XGBoost), it integrates Three-Way Decision (TWD) with XGBoost. The framework constructs a three-dimensional classification space of the positive domain (coupon usage), boundary domain (uncertain users), and negative domain (non-coupon usage) by introducing the deferred decision mechanism of TWD, combined with misclassification costs and learning costs to optimize decision boundaries. The author made experiments based on real consumption data, which is from Alibaba's Tianchi platform, the result shows TWD-XGBoost significantly improves accuracy (0.7560 vs. 0.7349) and F1 score (0.8230 vs. 0.8033) compared to traditional algorithms such as XGBoost and Random Forest. The study provides a new theoretical paradigm for user behavior prediction in high-noise scenarios.