改进规则模型紧凑性与歧义性，提升可解释性。
Improving Compactness and Reducing Ambiguity of CFIRE Rule-Based Explanations
- 后处理剪枝去除低贡献或冲突覆盖的规则
- 规则数量减少，类别歧义显著降低
- 适合需要高透明度的表格数据决策场景
基于表格数据的模型广泛应用于敏感领域，对可解释性提出更高要求。CFIRE 是一种近期提出的算法，通过局部解释构建紧凑的替代规则模型。尽管有效，但可能将不同类别的规则分配给同一样本，造成歧义。本文分析该歧义现象，提出一种后处理剪枝策略，移除低贡献或存在覆盖冲突的规则，从而在保持预测性能的前提下，获得更小、更清晰的规则模型。多组实验验证了该方法在多个数据集上的有效性，显著提升了规则模型的紧凑性与明确性。
原文摘要 · Abstract (English)
Models trained on tabular data are widely used in sensitive domains, increasing the demand for explanation methods to meet transparency needs. CFIRE is a recent algorithm in this domain that constructs compact surrogate rule models from local explanations. While effective, CFIRE may assign rules associated with different classes to the same sample, introducing ambiguity. We investigate this ambiguity and propose a post-hoc pruning strategy that removes rules with low contribution or conflicting coverage, yielding smaller and less ambiguous models while preserving fidelity. Experiments across multiple datasets confirm these improvements with minimal impact on predictive performance.
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