提出一种新交叉验证方法，避免数据重复使用，提升评估可靠性。
Irredundant $k$-Fold Cross-Validation
- 每条数据仅训练和测试一次，消除传统方法的冗余
- 性能估计方差更小，计算成本显著降低
- 适用于任意分类器，适合对评估稳定性要求高的研究
传统k折交叉验证中，每个样本在训练中被使用(k-1)次，测试1次，导致数据冗余，使部分样本过度影响学习过程。本文提出不可冗余k折交叉验证，确保在整个验证过程中每个样本恰好用于一次训练和一次测试。该方法实现数据更均衡利用，缓解因样本重复引起的过拟合问题，并提升模型对比分析的精确性。方法保持分层策略，且与分类器无关，兼容任意模型。实验表明，其性能估计在多种数据集上与传统k折相当，但方差更小（因训练集无重叠），且整体计算成本大幅下降。
原文摘要 · Abstract (English)
In traditional k-fold cross-validation, each instance is used ($k-1$) times for training and once for testing, leading to redundancy that lets many instances disproportionately influence the learning phase. We introduce Irredundant $k$-fold cross-validation, a novel method that guarantees each instance is used exactly once for training and once for testing across the entire validation procedure. This approach ensures a more balanced utilization of the dataset, mitigates overfitting due to instance repetition, and enables sharper distinctions in comparative model analysis. The method preserves stratification and remains model-agnostic, i.e., compatible with any classifier. Experimental results demonstrate that it delivers consistent performance estimates across diverse datasets -- comparable to $k$-fold cross-validation -- while providing less optimistic variance estimates because training partitions are non-overlapping, and significantly reducing the overall computational cost.
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