用多目标优化解决数据不平衡问题，提升少数类分类效果。
Sampling Imbalanced Data with Multi-objective Bilevel Optimization
- 构建双层优化框架，同时处理合成扩增和多数类降采样。
- 提出新度量指标，实验显示F1分数提升1-15%。
- 适合处理类别严重失衡的分类任务，如医疗诊断、异常检测。
二分类问题常因多数类与少数类样本数量不均，导致对少数类分类性能差。传统方法如损失加权或简单重采样易引发过拟合，且未考虑多数类与少数类之间的多样性差异。现有缺乏可衡量不平衡对模型影响的指标，使该问题难以解决。为此，本文提出两个关键贡献：首先，引入MOODS（多目标数据采样优化）框架，一种新型多目标双层优化方法，用于指导合成扩增与多数类降采样；其次，提出新的验证指标——ε/δ非重叠多样性度量，量化采样方法对模型性能的促进作用。实验表明，该方法在多样性驱动下实现当前最优表现，F1分数提升1%-15%。
原文摘要 · Abstract (English)
Two-class classification problems are often characterized by an imbalance between the number of majority and minority datapoints resulting in poor classification of the minority class in particular. Traditional approaches, such as reweighting the loss function or naïve resampling, risk overfitting and subsequently fail to improve classification because they do not consider the diversity between majority and minority datasets. Such consideration is infeasible because there is no metric that can measure the impact of imbalance on the model. To obviate these challenges, we make two key contributions. First, we introduce MOODS~(Multi-Objective Optimization for Data Sampling), a novel multi-objective bilevel optimization framework that guides both synthetic oversampling and majority undersampling. Second, we introduce a validation metric -- `$ε/ δ$ non-overlapping diversification metric' -- that quantifies the goodness of a sampling method towards model performance. With this metric we experimentally demonstrate state-of-the-art performance with improvement in diversity driving a $1-15 \%$ increase in $F1$ scores.
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