mlsanityData to 7 Oct 2026

Data Augmentation for Imbalanced Regression

arXiv 2302.09288 ↗First posted 18 Feb 2023stat.ML cs.LG stat.MEcited by 7

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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.

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

Builds on

7 of 39 references.

References · 7

  1. 2019Modeling Tabular data using Conditional GANcited by 695
  2. 2021Delving into Deep Imbalanced Regressioncited by 157
  3. 2016UBL: an R package for Utility-based Learningcited by 12
  4. 2021Synthetic Dataset Generation of Driver Telematicscited by 8
  5. 2021Rejective Sampling, Rerandomization and Regression Adjustment in Survey Experimentscited by 2
  6. 2019Regression Discontinuity Design under Self-selectioncited by 1
  7. 2021Covariate Adjustment in Regression Discontinuity Designscited by 1

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Cited by year

Citing papers per year: 2023: 2; 2024: 4; 2025: 1.

7 papers.

  1. 2024Data Augmentation with Variational Autoencoder for Imbalanced Datasetcited by 5
  2. 2023Generalized Oversampling for Learning from Imbalanced datasets and Associated Theorycited by 4
  3. 2025Local distribution-based adaptive oversampling for imbalanced regressioncited by 3
  4. 2024Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasetscited by 2
  5. 2023Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalancecited by 1
  6. 2024Review of Data-centric Time Series Analysis from Sample, Feature, and Periodcited by 1
  7. 2024Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regressioncited by 1