用优化方法筛选关键多数类样本，提升不平衡数据分类效果。
A Bilevel Optimization Framework for Imbalanced Data Classification
- 基于模型损失改进能力选择保留的多数类样本
- 实验显示F1分数最高比现有方法高10%
- 适合处理样本不均衡的分类任务
数据重平衡技术（如过采样和欠采样）是应对不平衡数据挑战的常用方法。为解决过采样导致噪声与重叠、随机欠采样导致欠拟合的问题，本文提出一种新欠采样方法：不随机删除多数类样本，而是根据其能否降低模型损失来决定是否保留。以模型损失改善为代理指标，评估每个样本对分类性能的影响，拒绝无法提升损失的样本。该方法剔除冗余多数类样本，从而找到最优多数类训练子集。算法的选取/拒绝机制源自一个独特的双层优化问题，旨在识别理想的训练集。实验表明，所提方法在F1分数上最高可比现有最优方法高出10%。
原文摘要 · Abstract (English)
Data rebalancing techniques, including oversampling and undersampling, are a common approach to addressing the challenges of imbalanced data. To tackle unresolved problems related to both oversampling and undersampling, we propose a new undersampling approach that: (i) avoids the pitfalls of noise and overlap caused by synthetic data and (ii) avoids the pitfall of under-fitting caused by random undersampling. Instead of undersampling majority data randomly, our method undersamples datapoints based on their ability to improve model loss. Using improved model loss as a proxy measurement for classification performance, our technique assesses a datapoint's impact on loss and rejects those unable to improve it. In so doing, our approach rejects majority datapoints redundant to datapoints already accepted and, thereby, finds an optimal subset of majority training data for classification. The accept/reject component of our algorithm is motivated by a bilevel optimization problem uniquely formulated to identify the optimal training set we seek. Experimental results show our proposed technique with F1 scores up to 10% higher than state-of-the-art methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。