发表机构
Macquarie University（麦考瑞大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究校准中错误置信集中问题，引入FALCON-Discover框架，利用多种差异信号排序预测。通过多数据集和强学习器实验发现，错误置信集中依赖模式，基于差异排序在强模式下表现优，最佳检测器因数据集而异，推动针对特定区域的校准策略。
AI 中文摘要
校准通常是整体评估的，但最危险的失败往往是局部的：预测即使错误却仍高度自信。我们将这种失败模式研究为错误置信集中，即自信的错误在预测空间中占据紧凑、可发现区域的程度。我们引入了FALCON-Discover，这是一个事后的、与模型无关的框架，它使用来自置信度、局部支持、邻域一致性和扰动稳定性的差异信号对预测进行排序。在七个二元表格数据集、四个种子、五重交叉拟合以及包括XGBoost和CatBoost在内的强学习器上，我们发现错误置信集中是反复出现的但依赖于模式。在主要置信阈值处，基于差异的排序在最强模式下显著优于最强的验证选择校准或信任评分基线，而原始置信度恢复的危险错误量很少。最佳检测器因数据集而异：当必须组合多个线索时，学习到的差异最强，而当局部决策脆弱性占主导时，以稳定性为中心的排序效果最佳。这些结果表明，危险的过度自信最好作为一个家族级发现问题来处理，而不是作为一个单分数校准问题，并推动了明确针对置信度、支持和稳定性发散区域的校准策略。
英文摘要
Calibration is usually evaluated in aggregate, but the most dangerous failures are often local: predictions that remain highly confident despite being wrong. We study this failure mode as false-confidence concentration, the extent to which confident errors occupy compact, discoverable regions of prediction space. We introduce FALCON-Discover, a post-hoc, model-agnostic framework that ranks predictions using discrepancy signals from confidence, local support, neighborhood agreement, and perturbation stability. Across seven binary tabular datasets, four seeds, five-fold cross-fitting, and strong learners including XGBoost and CatBoost, we find that false-confidence concentration is recurrent but regime-dependent. At the main confidence threshold, discrepancy-based ranking substantially outperforms the strongest validation-selected calibration or trust-scoring baseline in the strongest regimes, while raw confidence recovers little dangerous-error mass. The best detector varies across datasets: learned discrepancy is strongest when multiple cues must be combined, whereas stability-centered ranking works best when local decisional fragility dominates. These results show that dangerous overconfidence is better treated as a family-level discovery problem than as a single-score calibration problem, and motivate calibration strategies that explicitly target regions where confidence, support, and stability diverge.
