# Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory

arXiv: https://arxiv.org/abs/2308.02966 · HTML: https://mlsanity.com/paper/2308.02966/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 75 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Rethinking the Value of Labels for Improving Class-Imbalanced Learning](https://mlsanity.com/paper/2006.07529/) (2020)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [Balanced MSE for Imbalanced Visual Regression](https://mlsanity.com/paper/2203.16427/) (2022)
- [VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data](https://mlsanity.com/paper/2006.11941/) (2020)
- [C-Mixup: Improving Generalization in Regression](https://mlsanity.com/paper/2210.05775/) (2022)
- [RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression](https://mlsanity.com/paper/2205.15236/) (2022)
- [UBL: an R package for Utility-based Learning](https://mlsanity.com/paper/1604.08079/) (2016)
- [Clustered Federated Learning via Generalized Total Variation Minimization](https://mlsanity.com/paper/2105.12769/) (2021)
- [Data Augmentation for Imbalanced Regression](https://mlsanity.com/paper/2302.09288/) (2023)
- [A Self-Adaptive Synthetic Over-Sampling Technique for Imbalanced Classification](https://mlsanity.com/paper/1911.11018/) (2019)

## Built on by (most cited first, 4 of 4)

- [Local distribution-based adaptive oversampling for imbalanced regression](https://mlsanity.com/paper/2504.14316/) (2025)
- [Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression](https://mlsanity.com/paper/2508.13829/) (2025)
- [Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering](https://mlsanity.com/paper/2507.10088/) (2025)
- [DADIR: Density-Aware Data-level Imbalanced Regression Framework](https://mlsanity.com/paper/2607.17178/) (2026)

## Abstract (arXiv)

In supervised learning, it is quite frequent to be confronted with real imbalanced datasets. This situation leads to a learning difficulty for standard algorithms. Research and solutions in imbalanced learning have mainly focused on classification tasks. Despite its importance, very few solutions exist for imbalanced regression. In this paper, we propose a data augmentation procedure, the GOLIATH algorithm, based on kernel density estimates which can be used in classification and regression. This general approach encompasses two large families of synthetic oversampling: those based on perturbations, such as Gaussian Noise, and those based on interpolations, such as SMOTE. It also provides an explicit form of these machine learning algorithms and an expression of their conditional densities, in particular for SMOTE. New synthetic data generators are deduced. We apply GOLIATH in imbalanced regression combining such generator procedures with a wild-bootstrap resampling technique for the target values. We evaluate the performance of the GOLIATH algorithm in imbalanced regression situations. We empirically evaluate and compare our approach and demonstrate significant improvement over existing state-of-the-art techniques.