QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification

The authors propose QC-SMOTE, a quality-controlled oversampling framework designed to address the generation of low-quality synthetic samples in noisy or overlapping regions common in imbalanced classification tasks. This method estimates minority sample reliability using a composite neighborhood trustworthiness score and employs an IPQ-guided best-of-K strategy for generating synthetic candidates.

• QC-SMOTE combines local density, safe-level, and isolation from the majority class to estimate minority sample reliability.
• Synthetic candidates are generated using an IPQ-guided best-of-K strategy that evaluates midpoint purity and majority clearance.
• Generation behavior adapts across overlap-imbalance regimes by adjusting interpolation range and selection criteria to match local data geometry.
• Low-quality synthetic samples are replaced with original minority duplicates when neighborhood purity falls below an adaptive threshold.

Experiments on 30 imbalanced datasets demonstrate that QC-SMOTE achieves the strongest average AUC-ROC and Macro F1 among compared oversampling methods, particularly under moderate and severe imbalance conditions.