你还在为回归数据分布不均发愁？这个新框架DADIR用密度指导分区和生成，比固定分区法更准，专门提升稀疏区域的预测效果。
DADIR是一种密度感知数据级不平衡回归框架，专门处理连续目标变量中密度分布不均的问题。它包含三个组件：密度感知自适应分区（DAAP）根据密度变化递归划分目标空间；密度正则化条件变分自编码器（DR-CVAE）保留稀疏区域表示并学习潜在特征；潜在空间数据平衡通过特征聚类和过采样生成结构一致的合成样本。实验在多个不平衡回归数据集上验证，在欠表示区域和整体精度上都取得一致性提升。
DADIR: Density-Aware Data-level Imbalanced Regression Framework
Imbalanced learning addresses predictive modeling problems with underrepresented regions of the data distribution. Although widely studied in classification, imbalanced regression remains challenging because of continuous target variables and heterogeneous density distributions. Existing data-level methods often rely on fixed target partitioning or synthetic sample generation without jointly considering density variations and local feature-space structure. We propose DADIR, a Density-Aware Data-level Imbalanced Regression framework that exploits density information throughout the balancing process. DADIR comprises three components: (1) Density-Aware Adaptive Partitioning (DAAP), which recursively partitions the target space according to density variations; (2) a Density-Regularized Conditional Variational Autoencoder (DR-CVAE), which preserves sparse-region representations while learning latent features; and (3) latent-space data balancing, which combines feature-level clustering with oversampling to generate structurally consistent synthetic samples. Together, these components identify minority regions more effectively, preserve sparse-region information, and generate realistic synthetic data. The resulting balanced dataset can be used directly with existing regression models without modifying their architecture or learning objective. Experiments on diverse imbalanced regression datasets demonstrate consistent improvements in predictive performance, particularly in underrepresented regions, while also improving overall accuracy.