用户可动态调整约束条件，系统高效更新反事实解释。
UGCE: User-Guided Incremental Counterfactual Exploration
- 基于遗传算法增量更新反事实解释，避免重复计算。
- 在5个数据集上效率提升显著，保持高质量解。
- 适合需要交互式调试模型决策的场景。
反事实解释（CFEs）通过识别能改变模型输出的最小特征变更来解释机器学习预测。但在实际应用中，用户常随时间调整可行性约束，要求反事实生成具备动态适应能力。现有方法无法支持迭代更新，每次约束变化都需从头重新计算，效率低下且僵化。本文提出用户引导的增量反事实探索框架（UGCE），基于遗传算法实现反事实的增量更新。在五个基准数据集上的实验表明，与静态非增量方法相比，UGCE显著提升计算效率并保持高质量解。评估还显示，UGCE在不同约束序列下表现稳定，得益于高效的热启动策略，并揭示了不同约束类型对搜索行为的影响。
原文摘要 · Abstract (English)
Counterfactual explanations (CFEs) are a popular approach for interpreting machine learning predictions by identifying minimal feature changes that alter model outputs. However, in real-world settings, users often refine feasibility constraints over time, requiring counterfactual generation to adapt dynamically. Existing methods fail to support such iterative updates, instead recomputing explanations from scratch with each change, an inefficient and rigid approach. We propose User-Guided Incremental Counterfactual Exploration (UGCE), a genetic algorithm-based framework that incrementally updates counterfactuals in response to evolving user constraints. Experimental results across five benchmark datasets demonstrate that UGCE significantly improves computational efficiency while maintaining high-quality solutions compared to a static, non-incremental approach. Our evaluation further shows that UGCE supports stable performance under varying constraint sequences, benefits from an efficient warm-start strategy, and reveals how different constraint types may affect search behavior.
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