用更少约束提升信贷审批反事实解释的效率与真实性
Constraint-Reduced MILP with Local Outlier Factor Modeling for Plausible Counterfactual Explanations in Credit Approval
- 重构DACE框架，减少局部离群因子目标的约束数量
- 求解速度更快，解释质量保持不变
- 适合需要高效可解释性的金融风控场景
反事实解释（CE）是一种广泛使用的后处理方法，为个体提供改变机器学习模型不利预测的可操作建议。真实感反事实解释方法通过考虑数据分布特性提升合理性，但其优化模型引入大量约束，导致计算成本高。本文重新审视DACE框架，提出一种改进的混合整数线性规划（MILP）公式，显著减少了局部离群因子（LOF）目标部分的约束数量。实验采用线性SVM分类器与标准缩放器，结果表明该方法在保持解释质量的同时大幅降低求解时间。这展示了更高效LOF建模在反事实解释与数据科学中的潜力。
原文摘要 · Abstract (English)
Counterfactual explanation (CE) is a widely used post-hoc method that provides individuals with actionable changes to alter an unfavorable prediction from a machine learning model. Plausible CE methods improve realism by considering data distribution characteristics, but their optimization models introduce a large number of constraints, leading to high computational cost. In this work, we revisit the DACE framework and propose a refined Mixed-Integer Linear Programming (MILP) formulation that significantly reduces the number of constraints in the local outlier factor (LOF) objective component. We also apply the method to a linear SVM classifier with standard scaler. The experimental results show that our approach achieves faster solving times while maintaining explanation quality. These results demonstrate the promise of more efficient LOF modeling in counterfactual explanation and data science applications.
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