直接优化决策净收益，打造可解释的风险评分系统。
Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit
- 将风险评分建模为稀疏整数规划，系数为整数便于理解。
- 在多个数据集上实现高净收益，同时保持良好区分度和校准性。
- 适合需要透明决策的医疗、金融等高风险领域使用。
风险评分系统广泛应用于高风险决策场景。然而，现有方法多聚焦于预测准确率或似然准则，可能与最大化实用价值的目标不一致。本文提出一种新型风险评分系统，直接在多个决策阈值上优化净收益。模型被表述为稀疏整数线性规划问题，可构建具有整数系数的透明评分体系，便于解释与应用。我们还建立了净收益、区分度与校准性之间的基本关系。理论分析证明，优化净收益可保证传统性能指标。我们在多个公开数据集及大规模信用风险数据集上评估了该方法。计算结果表明，该可解释方法在实现高净收益的同时，保持了竞争力的区分度与校准性能。
原文摘要 · Abstract (English)
Risk scoring systems are widely used in high-stakes domains to assist decision-making. However, existing approaches often focus on optimizing predictive accuracy or likelihood-based criteria, which may not align with the main goal of maximizing utility. In this paper, we propose a novel risk scoring system that directly optimizes net benefit over a range of decision thresholds. The model is formulated as a sparse integer linear programming problem which enables the construction of a transparent scoring system with integer coefficients, and hence, facilitates interpretation and practical application. We also establish fundamental relationships among net benefit, discrimination, and calibration. Our analysis proves that optimizing net benefit also guarantees conventional performance measures. We evaluated our method on multiple public datasets as well as on a large-scale credit risk dataset. This computational study demonstrated that our interpretable method can effectively achieve high net benefit while maintaining competitive discrimination and calibration performance.
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