动态调整折叠次数，让模型评估更快更省电。
From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation
- 根据评估结果稳定情况自动停止，减少不必要的计算。
- 平均少用4次折叠，节省约40%计算资源和能耗。
- 适合追求效率又不牺牲精度的机器学习实践者。
本文提出e-fold交叉验证，一种节能的替代k折交叉验证的方法。该方法基于停止准则动态调整折叠数量：每轮评估后检查已评估折叠的标准差是否持续下降或保持稳定，一旦满足即提前终止。在15个数据集和10种机器学习算法上测试，e-fold平均比10折少4次折叠，使评估时间、计算资源和能源消耗降低约40%。在较大数据集上，e-fold与10折性能差异小于2%，复杂模型的差距更小。96%的迭代结果落在置信区间内，证实其统计可靠性。e-fold交叉验证在保持相近精度的同时显著降低计算成本，是一种可靠高效的替代方案。
原文摘要 · Abstract (English)
This paper introduces e-fold cross-validation, an energy-efficient alternative to k-fold cross-validation. It dynamically adjusts the number of folds based on a stopping criterion. The criterion checks after each fold whether the standard deviation of the evaluated folds has consistently decreased or remained stable. Once met, the process stops early. We tested e-fold cross-validation on 15 datasets and 10 machine-learning algorithms. On average, it required 4 fewer folds than 10-fold cross-validation, reducing evaluation time, computational resources, and energy use by about 40%. Performance differences between e-fold and 10-fold cross-validation were less than 2% for larger datasets. More complex models showed even smaller discrepancies. In 96% of iterations, the results were within the confidence interval, confirming statistical significance. E-fold cross-validation offers a reliable and efficient alternative to k-fold, reducing computational costs while maintaining comparable accuracy.
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