这篇论文提出了QC-SMOTE，在30个数据集上比传统SMOTE获得更好的AUC和F1分数，特别适合处理中度到高度不平衡的数据。
QC-SMOTE提出一种质量控制过采样框架，通过复合邻域信任度评分估计少数类样本可靠性。生成候选样本时采用IPQ引导的最佳K策略，评估中点纯度并考虑多数类清除。方法根据重叠-不平衡程度自适应调整插值范围和选择标准。在30个不平衡数据集上的重复分层交叉验证显示，QC-SMOTE在平均AUC-ROC和Macro F1上优于对比过采样方法，在中度和严重不平衡下提升尤为明显。
QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification
Class imbalance poses a significant challenge in classification, where existing methods such as SMOTE often generate low-quality synthetic samples in regions with noise or class overlap. We propose QC-SMOTE, a quality-controlled oversampling framework that estimates minority sample reliability using a composite neighbourhood trustworthiness score combining local density, safe-level, and isolation from the majority class. Synthetic candidates are generated using an IPQ-guided best-of-K strategy that evaluates midpoint purity and, when required, majority clearance, with allocation guided by sample reliability and boundary informativeness. Generation behaviour adapts across overlap--imbalance regimes, adjusting interpolation range and selection criteria to match local data geometry. Low-quality synthetic samples are replaced with original minority duplicates when neighbourhood purity falls below an adaptive threshold, providing graceful degradation by reverting to duplication in severely noisy regions. Experiments on 30 imbalanced datasets using repeated stratified cross-validation show that QC-SMOTE achieves the strongest average AUC-ROC and Macro F1 among the compared oversampling methods, with particularly clear gains under moderate and severe imbalance. These results demonstrate the importance of quality-aware, geometry-adaptive synthetic sampling for robust imbalanced classification.