用数学变换把多数类样本转成少数类，提升不平衡数据分类效果
Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification
- 设计参数化变换模型，通过MMD对齐多数与少数类全局分布
- 引入三元组损失，让生成样本聚焦难分边界区域，提升区分度
- 在29个数据集上超越传统和生成方法，适合医疗、反欺诈等关键场景
监督分类中的类别不平衡常导致模型偏向多数类，尤其在医疗诊断和反欺诈等关键应用中表现不佳。传统过采样方法如SMOTE通过局部插值生成少数类样本，难以捕捉高维空间的全局分布；基于GAN的深度生成模型虽能建模更丰富分布，但在严重不平衡下易出现训练不稳定和模式崩溃。为此，本文提出一种过采样框架，学习一个参数化变换，将多数类样本映射至少数类分布。该方法通过最小化变换后样本与真实少数类样本之间的最大均值差异（MMD）实现全局对齐，并引入三元组损失正则项，引导合成样本靠近困难边界区域以增强判别能力。我们在29个合成与真实数据集上评估了该方法，在AUROC、G-mean、F1-score和MCC指标上均优于经典及生成基线。结果表明该框架具备鲁棒性、计算高效且具实际应用价值。
原文摘要 · Abstract (English)
Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class, particularly in critical applications such as medical diagnosis and fraud detection. Traditional oversampling techniques, including SMOTE and its variants, generate synthetic minority samples via local interpolation but fail to capture global data distributions in high-dimensional spaces. Deep generative models based on GANs offer richer distribution modeling yet suffer from training instability and mode collapse under severe imbalance. To overcome these limitations, we introduce an oversampling framework that learns a parametric transformation to map majority samples into the minority distribution. Our approach minimizes the maximum mean discrepancy (MMD) between transformed and true minority samples for global alignment, and incorporates a triplet loss regularizer to enforce boundary awareness by guiding synthesized samples toward challenging borderline regions. We evaluate our method on 29 synthetic and real-world datasets, demonstrating consistent improvements over classical and generative baselines in AUROC, G-mean, F1-score, and MCC. These results confirm the robustness, computational efficiency, and practical utility of the proposed framework for imbalanced classification tasks.
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