用解耦变分自编码器改进不平衡回归的数据生成方法
Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression
- 在潜在空间中结合解耦VAE与平滑自助法生成新数据
- 在基准数据集上显著提升不平衡回归的预测性能
- 适合处理表格型数据的不公平分布回归问题
不平衡分布学习是预测建模中的常见且重要挑战，常导致标准算法性能下降。尽管已有多种方法应对该问题，但多数针对分类任务，对回归的关注较少。本文提出一种新方法，用于改善表格数据在不平衡回归（IR）框架下的学习效果。我们利用变分自编码器（VAEs）建模并定义数据分布的潜在表示。然而，VAE在不均衡数据上的效率与其他标准方法类似受限。为此，我们开发了一种创新的数据生成方法，将解耦VAE与在潜在空间中应用的平滑自助法相结合。通过与竞争方法在不平衡回归基准数据集上的数值对比，评估了该方法的有效性。
原文摘要 · Abstract (English)
Imbalanced distribution learning is a common and significant challenge in predictive modeling, often reducing the performance of standard algorithms. Although various approaches address this issue, most are tailored to classification problems, with a limited focus on regression. This paper introduces a novel method to improve learning on tabular data within the Imbalanced Regression (IR) framework, which is a critical problem. We propose using Variational Autoencoders (VAEs) to model and define a latent representation of data distributions. However, VAEs can be inefficient with imbalanced data like other standard approaches. To address this, we develop an innovative data generation method that combines a disentangled VAE with a Smoothed Bootstrap applied in the latent space. We evaluate the efficiency of this method through numerical comparisons with competitors on benchmark datasets for IR.
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