arXiv:2606.24625v1 Announce Type: new 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, ma
The paper addresses a long-standing challenge in machine learning, class imbalance, which is critical for real-world AI applications where data distributions are often skewed.
Improved handling of imbalanced datasets can lead to more robust and reliable AI systems, especially in high-stakes domains like fraud detection or medical diagnosis, benefiting sectors reliant on accurate classification.
This advancement provides a more sophisticated method for generating synthetic data, potentially reducing biases and improving model performance in scenarios where minority classes are underrepresented.
- · AI/ML researchers
- · Data scientists
- · Industries with imbalanced datasets (e.g., finance, healthcare)
More accurate classification models will be developed across various applications.
Improved model reliability could increase user trust and adoption of AI systems in critical fields.
Reduced false negatives in areas like medical diagnosis or anomaly detection could have significant societal and economic benefits.
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