DADIR: Density-Aware Data-level Imbalanced Regression Framework

• URL: http://arxiv.org/abs/2607.17178v1
• Date: Sun, 19 Jul 2026 10:23:27 GMT
• Title: DADIR: Density-Aware Data-level Imbalanced Regression Framework
• Abstract summary: DADIR is a Density-Aware Data-level Imbalanced Regression framework.<n>It exploits density information throughout the balancing process.<n>It identifies minority regions more effectively, preserve sparse-region information, and generate realistic synthetic data.
• Score: 1.3700362496838856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Geometry-Aware Dataset Condensation for Diffusion Model Training [103.45641113998839]
  We propose to reformulate real subset selection as a geometry-aware distribution alignment problem.<n>By incorporating one-sided partial optimal transport, our method selectively aligns a compact subset with the full data distribution.<n>Experiments across diffusion variants, subset sizes, image resolutions, and training rounds show that our method achieves superior fidelity and distributional coverage.
  arXiv  Detail & Related papers  (2026-06-04T08:53:58Z)
• Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing [1.3700362496838856]
  Imbalanced learning is a critical challenge in machine learning, where underrepresented target values can bias models and degrade prediction performance on rare but important cases.<n>We propose a unified hybrid framework that integrates both data- and algorithm-level balancing strategies into a regressor-agnostic pipeline.<n> Experimental results on benchmark datasets demonstrate that the proposed framework consistently improves predictive performance compared to standalone regressors and existing imbalanced regression approaches.
  arXiv  Detail & Related papers  (2026-05-31T13:13:10Z)
• SEED: Targeted Data Selection by Weighted Independent Set [76.68391670109433]
  We develop a robust and scalable data selection pipeline dubbed SEED.<n>SEED consistently outperforms state-of-the-art methods on instruction tuning, visual instruction tuning, and semantic segmentation.
  arXiv  Detail & Related papers  (2026-05-15T07:26:54Z)
• Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs [77.42033827176806]
  One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm.<n>We propose a novel Federated Model Inversion and Token Relabel framework, which trains the global model by fully exploiting all patches of synthetic images.
  arXiv  Detail & Related papers  (2026-05-11T15:49:08Z)
• Rare-Aware Autoencoding: Reconstructing Spatially Imbalanced Data [49.13338489780734]
  Autoencoders can be challenged by spatially non-uniform sampling of image content.<n>In practice, autoencoders are biased toward dominant patterns resulting in the loss of fine-grained detail.<n>We address spatial imbalance by two complementary components: (i) self-entropy-based loss that upweights statistically uncommon spatial locations.
  arXiv  Detail & Related papers  (2026-04-02T13:38:29Z)
• Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation [109.13471554184554]
  We reformulate dataset distillation as an Optimal Transport (OT) distance minimization problem.<n>OT offers a geometrically faithful framework for distribution matching.<n>Our method consistently outperforms state-of-the-art approaches in an efficient manner.
  arXiv  Detail & Related papers  (2025-11-29T04:04:05Z)
• More Data or Better Algorithms: Latent Diffusion Augmentation for Deep Imbalanced Regression [0.0]
  In many real-world regression tasks, the data distribution is heavily skewed, and models learn predominantly from abundant majority samples.<n>We propose LatentDiff, a novel framework that uses conditional diffusion models with priority-based generation to synthesize high-quality features in the latent representation space.<n>Experiments on three DIR benchmarks demonstrate substantial improvements in minority regions while maintaining overall accuracy.
  arXiv  Detail & Related papers  (2025-09-27T10:44:28Z)
• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• Hyperbolic Dataset Distillation [44.63243875072762]
  We propose a novel hyperbolic dataset distillation method.<n>We find that pruning in hyperbolic space requires only 20% of the distilled core set to retain model performance.<n>This is the first work to incorporate the hyperbolic space into the dataset distillation process.
  arXiv  Detail & Related papers  (2025-05-30T14:14:00Z)
• SMOGAN: Synthetic Minority Oversampling with GAN Refinement for Imbalanced Regression [0.0]
  Imbalanced regression refers to prediction tasks where the target variable is skewed.<n>This skewness hinders machine learning models, especially neural networks, which concentrate on dense regions.<n>We propose SMOGAN, a two-step oversampling framework for imbalanced regression.
  arXiv  Detail & Related papers  (2025-04-29T20:15:25Z)
• Local distribution-based adaptive oversampling for imbalanced regression [0.0]
  Imbalanced regression occurs when continuous target variables have skewed distributions, creating sparse regions.<n>We propose LDAO (Local Distribution-based Adaptive Oversampling), a novel data-level approach that avoids categorizing individual samples as rare or frequent.<n> LDAO achieves a balanced representation across the entire target range while preserving the inherent statistical structure within each local distribution.
  arXiv  Detail & Related papers  (2025-04-19T14:36:41Z)
• Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression [2.435853975142516]
  In real-world regression tasks, datasets frequently exhibit imbalanced distributions, characterized by a scarcity of data in high-complexity regions and an abundance in low-complexity areas.<n>We introduce a novel concept of Imbalanced Regression, which takes into account both the complexity of the problem and the density of data points, extending beyond traditional definitions that focus only on data density.<n>We propose Error Distribution Smoothing (EDS) as a solution to tackle imbalanced regression, effectively selecting a representative subset from the dataset to reduce redundancy while maintaining balance and representativeness.
  arXiv  Detail & Related papers  (2025-02-04T12:40:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.