用扩散模型生成少数标签样本，提升多标签学习效率与平衡性。
Addressing Multilabel Imbalance with an Efficiency-Focused Approach Using Diffusion Model-Generated Synthetic Samples
- 设计专用扩散模型MLDM，生成多标签数据中的少数类样本。
- 在多个数据集上实验，性能优于传统重采样方法且速度更快。
- 适合处理标签不平衡的多标签分类任务，尤其关注效率提升。
基于不平衡数据训练的预测模型往往产生偏差，这一问题在多标签学习（MLL）中尤为严重，因需同时处理多个输出标签。现有解决方案中，数据重采样是通用方法，通过生成少数标签的新样本填补特征空间空缺，从而改善模型表现。本文提出一种专为多标签学习设计的扩散模型——MLDM（MultiLabel Diffusion Model），利用扩散机制生成高质量合成样本。不同于以往主要应用于图像视频生成的扩散模型，本工作将其适配于多标签数据合成。实验对比了多种主流重采样算法，结果表明MLDM在分类性能上具有竞争力，同时显著提升了生成效率。
原文摘要 · Abstract (English)
Predictive models trained on imbalanced data tend to produce biased results. This problem is exacerbated when there is not just one output label, but a set of them. This is the case for multilabel learning (MLL) algorithms used to classify patterns, rank labels, or learn the distribution of outputs. Many solutions have been proposed in the literature. The one that can be applied universally, independent of the algorithm used to build the model, is data resampling. The generation of new instances associated with minority labels, so that empty areas of the feature space are filled, helps to improve the obtained models. The quality of these new instances depends on the algorithm used to generate them. In this paper, a diffusion model tailored to produce new instances for MLL data, called MLDM (\textit{MultiLabel Diffusion Model}), is proposed. Diffusion models have been mainly used to generate artificial images and videos. Our proposed MLDM is based on this type of models. The experiments conducted compare MLDM with several other MLL resampling algorithms. The results show that MLDM is competitive while it improves efficiency.
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