# Data Augmentation for Imbalanced Regression

arXiv: https://arxiv.org/abs/2302.09288 · HTML: https://mlsanity.com/paper/2302.09288/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 18 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [UBL: an R package for Utility-based Learning](https://mlsanity.com/paper/1604.08079/) (2016)
- [Synthetic Dataset Generation of Driver Telematics](https://mlsanity.com/paper/2102.00252/) (2021)
- [Rejective Sampling, Rerandomization and Regression Adjustment in Survey Experiments](https://mlsanity.com/paper/2109.09930/) (2021)
- [Regression Discontinuity Design under Self-selection](https://mlsanity.com/paper/1911.09248/) (2019)
- [Covariate Adjustment in Regression Discontinuity Designs](https://mlsanity.com/paper/2110.08410/) (2021)

## Built on by (most cited first, 7 of 7)

- [Data Augmentation with Variational Autoencoder for Imbalanced Dataset](https://mlsanity.com/paper/2412.07039/) (2024)
- [Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory](https://mlsanity.com/paper/2308.02966/) (2023)
- [Local distribution-based adaptive oversampling for imbalanced regression](https://mlsanity.com/paper/2504.14316/) (2025)
- [Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets](https://mlsanity.com/paper/2403.15790/) (2024)
- [Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance](https://mlsanity.com/paper/2307.12676/) (2023)
- [Review of Data-centric Time Series Analysis from Sample, Feature, and Period](https://mlsanity.com/paper/2404.16886/) (2024)
- [Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression](https://mlsanity.com/paper/2412.12327/) (2024)

## Abstract (arXiv)

In this work, we consider the problem of imbalanced data in a regression framework when the imbalanced phenomenon concerns continuous or discrete covariates. Such a situation can lead to biases in the estimates. In this case, we propose a data augmentation algorithm that combines a weighted resampling (WR) and a data augmentation (DA) procedure. In a first step, the DA procedure permits exploring a wider support than the initial one. In a second step, the WR method drives the exogenous distribution to a target one. We discuss the choice of the DA procedure through a numerical study that illustrates the advantages of this approach. Finally, an actuarial application is studied.