提出新方法评估预测难度，提升预测区间准确性
Target Strangeness: A Novel Conformal Prediction Difficulty Estimator
- 基于预测值邻近样本的目标分布，衡量其异常程度
- 在多个回归任务中优于现有最先进方法
- 适合需要可靠置信区间的实际应用
本文提出一种名为目标奇异度（Target Strangeness）的新颖困难度估计器，用于校准置信区间（PIs）。该方法通过评估预测值在其最近邻样本目标分布中的罕见程度，实现对预测不确定性的更准确建模。在多个共形回归实验中，该方法表现超越当前最优基准，显著提升了预测区间的可靠性与有效性。
原文摘要 · Abstract (English)
This paper introduces Target Strangeness, a novel difficulty estimator for conformal prediction (CP) that offers an alternative approach for normalizing prediction intervals (PIs). By assessing how atypical a prediction is within the context of its nearest neighbours' target distribution, Target Strangeness can surpass the current state-of-the-art performance. This novel difficulty estimator is evaluated against others in the context of several conformal regression experiments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。