改进了可共性预测的聚合方法，提升效率且保持准确性
Aggregation in conformal e-classification
- 提出更简单灵活的交叉共性e预测聚合思路
- 实验验证新方法在保持有效性的同时提升效率
- 适合关注预测可靠性与计算效率的研究者
对现有交叉共性e预测方法及其简化变体进行实验研究。该方法通过聚合多个共性e预测器，在不牺牲预测有效性的前提下，实现更好的预测与计算效率平衡。本文重点分析其性能表现，并提出更直观、更具灵活性的改进方案，验证了其在实际应用中的优势。
原文摘要 · Abstract (English)
Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least approximately. An important advantage of conformal e-predictors is that they are easier to aggregate without sacrificing their validity. This paper studies experimentally cross-conformal e-prediction, which is an existing method of aggregating conformal e-predictors, and its modifications that are conceptually simpler and more flexible.
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