提出SCD-split方法，让预测区间更连贯易懂。
Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability
- 在CP框架中引入平滑操作，合并分散的预测子区间。
- 实验显示新方法在区间长度和连通性间取得更好平衡。
- 适合需要可解释性且兼顾效率的机器学习应用。
同质预测（Conformal Prediction, CP）是一种无需分布假设的严格统计预测集构建框架。尽管如CD-split等流行变体提升了效率，但常产生多个不相连的子区间，难以解释。本文提出SCD-split，在CP框架中引入平滑操作，有望合并子区间，从而生成更易解释的预测集。在合成与真实数据集上的实验表明，SCD-split在区间长度与不连通子区间数量之间实现良好权衡。理论上，在特定条件下，SCD-split可证明减少不连通子区间数量，同时保持与CD-split相当的覆盖率和区间长度。
原文摘要 · Abstract (English)
Conformal Prediction (CP) is a distribution-free framework for constructing statistically rigorous prediction sets. While popular variants such as CD-split improve CP's efficiency, they often yield prediction sets composed of multiple disconnected subintervals, which are difficult to interpret. In this paper, we propose SCD-split, which incorporates smoothing operations into the CP framework. Such smoothing operations potentially help merge the subintervals, thus leading to interpretable prediction sets. Experimental results on both synthetic and real-world datasets demonstrate that SCD-split balances the interval length and the number of disconnected subintervals. Theoretically, under specific conditions, SCD-split provably reduces the number of disconnected subintervals while maintaining comparable coverage guarantees and interval length compared with CD-split.
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