NADOS: Reliability-Gated Difficulty-Aware Oversampling for Noisy Imbalanced Classification

Abstract

Class imbalance becomes more challenging when minority underrepresentation is accompanied by label noise, because synthetic oversampling may amplify unreliable local structures if noisy minority samples are used as interpolation seeds. This study proposes Noise-Aware Adaptive-Difficulty Oversampling (NADOS), which separates the assessment of seed trustworthiness from the allocation of synthesis effort. NADOS evaluates each minority instance through two local criteria. A reliability score determines whether the instance is suitable for synthetic generation, while a difficulty score assigns synthesis priority among eligible instances. The method is evaluated on 22 binary imbalanced benchmark datasets, five controlled label-noise levels, four classifier families, and nine oversampling methods. Across the full benchmark, NADOS obtains average F1 = 0.7428, G-mean = 0.8511, balanced accuracy = 0.8552, and AUPRC = 0.7700. These results give NADOS the strongest average F1, G-mean, and balanced accuracy, while remaining competitive on AUPRC. Non-parametric statistical tests show significant differences among the compared methods and indicate that NADOS is consistently competitive under noisy imbalanced learning conditions. Separating seed reliability from synthesis difficulty provides a practical strategy for noisy imbalanced classification. The reliability gate limits unsafe seed usage, while the difficulty score preserves attention to difficult but trustworthy minority samples.