QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification

2026-06-23

AI Summary

This study addresses the limitation of conventional oversampling methods like SMOTE, which often generate low-quality synthetic samples in noisy or class-overlapping regions under class imbalance. To overcome this, the authors propose a quality-controllable oversampling framework that evaluates the reliability of minority-class instances using a composite neighborhood credibility score—integrating local density, safety level, and isolation from majority classes. High-quality synthetic samples are then generated via an IPQ-guided Best-of-K selection strategy. Furthermore, the method adaptively adjusts interpolation ranges and selection criteria based on local data geometry, reverting to simple replication in low-purity regions to enhance robustness. Experimental results across 30 imbalanced datasets demonstrate that the proposed approach consistently outperforms existing oversampling techniques in terms of AUC-ROC and Macro F1, with particularly notable gains under moderate to severe class imbalance.

Abstract

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.

Problem

• class imbalance
• SMOTE
• synthetic samples
• noise
• class overlap

Innovation

• QC-SMOTE
• quality-controlled oversampling
• neighbourhood trustworthiness
• adaptive synthetic sampling
• class imbalance