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.

翻译：在敏感领域中，基于表格数据训练的模型应用广泛，这增加了对解释方法满足透明度需求的要求。CFIRE是该领域近期提出的一种算法，它从局部解释中构建紧凑的代理规则模型。尽管有效，CFIRE可能将不同类别关联的规则分配给同一样本，从而引入模糊性。我们研究了这种模糊性，并提出了一种事后剪枝策略，该策略移除贡献度低或覆盖范围冲突的规则，从而在保持保真度的同时，生成更小且模糊性更低的模型。在多个数据集上的实验证实了这些改进，且对预测性能的影响极小。