提出K-IPO方法，在数据扩增时保持特征重要性排序，避免模型解释失真。
K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data
- 先生成后筛选：迭代生成少数类样本，仅保留能维持特征重要性相关性的数据。
- 通过控制肯德尔相关系数τ，确保扩增后特征重要性排名与原数据一致。
- 适合注重模型可解释性的分类任务，尤其在医疗、金融等高风险场景中适用。
过采样广泛用于处理表格数据中的类别不平衡问题，但现有方法会扭曲模型解释所依赖的特征重要性排序。尽管近期研究通过比较真实与合成数据量化了这种失真，却未主动防范。本文提出肯德尔约束的重要性保持过采样（K-IPO），一种无需依赖生成器的“生成-选择”框架，在数据增强过程中保持原始数据的特征重要性排序。K-IPO 迭代生成少数类候选样本，仅当其加入后仍满足用户设定的最小肯德尔 τ 相关系数时才接受。可对重要性最高的特征施加更严格约束。我们在20个不平衡二分类数据集上，使用三种分类器和多种解释方法评估 K-IPO。多数情况下，K-IPO 在特征重要性保持、解释一致性及类别可分性方面表现最佳或并列最佳，同时提升预测性能，计算开销可控。
原文摘要 · Abstract (English)
Oversampling is widely used to address class imbalance in tabular classification, but existing methods can distort the feature importance ranking underlying model explanations. Although recent studies have quantified this distortion by comparing real and synthetic data, none have actively sought to prevent it. In this paper, we introduce Kendall-constrained Importance-Preserving Oversampling (K-IPO), a generator-agnostic, "generate-then-select" framework that preserves the original data's feature importance ranking during augmentation. K-IPO iteratively generates minority-class candidates and accepts them only if their inclusion maintains a user-defined minimum Kendall's tau (τ) correlation with the reference ranking. Optionally, stricter constraints can be applied to the highest-ranked features. We evaluated K-IPO on 20 imbalanced binary classification datasets using three classifiers and multiple explanation methods. In most cases, K-IPO achieved the best or tied-best results in feature importance preservation, explanation consistency, and class separability. It also generally improved predictive performance while maintaining competitive computational overhead.
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