发表机构
University of Sevilla（塞维利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出免模型特征重要性方法$\Psi$-Resilience，利用一维拓扑信号构建类不一致景观并聚合持久性特征，在合成与真实数据上分别达到0.8和0.9的Spearman相关性，实现无需预测模型的分布级审计。
AI 中文摘要
我们提出了 $\Psi$-Resilience，一种免模型的特征重要性方法，它通过一维拓扑信号直接从数据本身推导出解释。我们的方法通过估计类条件密度并沿特征轴取它们的逐点绝对差来构建类不一致景观。然后，该一维信号的0维持续性定义了一个弹性泛函，该泛函仅聚合那些在由用户设定的鲁棒性尺度下仍能存活的拓扑特征。这为我们提供了一个上下文鲁棒的重要性评分，该评分可通过底层的一维景观及其持续性进行固有审计。我们在合成数据集和真实数据集上评估了我们的方法。在具有指定真实重要性的合成生成器上，$\Psi$-Resilience 以高保真度恢复了特征的排序，实现了高达0.8的Spearman秩相关系数，并与包括SHAP和互信息在内的多种特征重要性方法表现相当。在没有已知真实标签的真实数据集上，我们的方法与这些方法一致，相关性高达0.9。这些结果表明，$\Psi$-Resilience 是一种稳定的解释方法，无需依赖预测模型即可实现对特征重要性的严格、分布级审计。
英文摘要
We introduce $Ψ$-Resilience, a model-free feature importance method that derives explanations directly from the data itself via 1D topological signals. Our method constructs a class-disagreement landscape by estimating class-conditional densities and taking their pointwise absolute difference along the feature axis. Then, the 0-dimensional persistence of this 1D signal defines a resilience functional that aggregates only those topological features that survive perturbations up to a robustness scale which is set by the user. This gives us a context-robust importance score that is inherently auditable via the underlying 1D landscapes and their persistence. We evaluate our method on both synthetic and real datasets. On synthetic generators with specified ground-truth importance, $Ψ$-Resilience recovers the ranking of features with high fidelity, achieving Spearman rank correlations up to 0.8 and performing competitively with multiple feature importance methods, including SHAP and mutual information. On real datasets with no known ground truth, our technique agrees with these methods, with correlations up to 0.9. These results show that $Ψ$-Resilience is a stable explanation method that enables rigorous, distribution-level auditing of feature importance without relying on a predictive model.
