用变分自编码器+平滑抽样增强不平衡回归数据，提升模型性能。
Data Augmentation with Variational Autoencoder for Imbalanced Dataset
- 结合VAE与平滑抽样生成合成数据，解决不平衡回归问题。
- 在模拟数据和真实不平衡回归数据集上表现优于对比方法。
- 适合处理表格型数据的回归任务，尤其当少数类样本稀缺时。
从不平衡分布中学习是预测建模中的重大挑战，通常导致标准算法性能下降。尽管已有多种方法应对该问题，但多数聚焦于分类任务，对回归问题关注较少。本文提出一种新方法，旨在提升表格数据在不平衡回归（IR）框架下的学习效果，该问题仍具挑战性。我们利用变分自编码器（VAE）——一种强大的合成数据生成工具，可建模并捕捉复杂分布的潜在表示。然而，传统VAE在处理不平衡回归时效率较低。为此，我们提出一种新方法，将VAE与平滑抽样相结合，专门设计以应对不平衡回归的难点。通过在模拟数据及已知的不平衡回归数据集上进行数值实验，系统评估该方法的有效性，并与现有方法对比。
原文摘要 · Abstract (English)
Learning from an imbalanced distribution presents a major challenge in predictive modeling, as it generally leads to a reduction in the performance of standard algorithms. Various approaches exist to address this issue, but many of them concern classification problems, with a limited focus on regression. In this paper, we introduce a novel method aimed at enhancing learning on tabular data in the Imbalanced Regression (IR) framework, which remains a significant problem. We propose to use variational autoencoders (VAE) which are known as a powerful tool for synthetic data generation, offering an interesting approach to modeling and capturing latent representations of complex distributions. However, VAEs can be inefficient when dealing with IR. Therefore, we develop a novel approach for generating data, combining VAE with a smoothed bootstrap, specifically designed to address the challenges of IR. We numerically investigate the scope of this method by comparing it against its competitors on simulations and datasets known for IR.
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