# LoRAS: An oversampling approach for imbalanced datasets

arXiv: https://arxiv.org/abs/1908.08346 · HTML: https://mlsanity.com/paper/1908.08346/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning](https://mlsanity.com/paper/2207.06084/) (2022)
- [Stop Oversampling for Class Imbalance Learning: A Critical Review](https://mlsanity.com/paper/2202.03579/) (2022)
- [Concentration and excess risk bounds for imbalanced classification with synthetic oversampling](https://mlsanity.com/paper/2510.20472/) (2025)
- [Machine Learning Applications for Therapeutic Tasks with Genomics Data](https://mlsanity.com/paper/2105.01171/) (2021)
- [An Asymmetric Contrastive Loss for Handling Imbalanced Datasets](https://mlsanity.com/paper/2207.07080/) (2022)
- [A multi-schematic classifier-independent oversampling approach for imbalanced datasets](https://mlsanity.com/paper/2107.07349/) (2021)
- [Convex space learning for tabular synthetic data generation](https://mlsanity.com/paper/2407.09789/) (2024)
- [From SMOTE to Mixup for Deep Imbalanced Classification](https://mlsanity.com/paper/2308.15457/) (2023)
- [Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification](https://mlsanity.com/paper/2601.01150/) (2026)
- [ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets](https://mlsanity.com/paper/2206.09812/) (2022)
- [Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data](https://mlsanity.com/paper/2407.04990/) (2024)
- [Data-Driven Differential Evolution in Tire Industry Extrusion: Leveraging Surrogate Models](https://mlsanity.com/paper/2507.11191/) (2025)
- [Geometric Manifold Rectification for Imbalanced Learning](https://mlsanity.com/paper/2602.13045/) (2026)
- [Structured Gaussian Processes for Uncertainty-Aware Classification of High-Dimensional, Small-Sampled Omics Data](https://mlsanity.com/paper/2607.02103/) (2026)
- [The Entropy Triangle Method (ETM): A novel framework for the prevention of cardiac arrhythmia with a review of more than 10,000 patients](https://mlsanity.com/paper/2609.29187/) (2026)

## Abstract (arXiv)

The Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the overall balance of the model. In this article, we present an approach that overcomes this limitation of SMOTE, employing Localized Random Affine Shadowsampling (LoRAS) to oversample from an approximated data manifold of the minority class. We benchmarked our algorithm with 14 publicly available imbalanced datasets using three different Machine Learning (ML) algorithms and compared the performance of LoRAS, SMOTE and several SMOTE extensions that share the concept of using convex combinations of minority class data points for oversampling with LoRAS. We observed that LoRAS, on average generates better ML models in terms of F1-Score and Balanced accuracy. Another key observation is that while most of the extensions of SMOTE we have tested, improve the F1-Score with respect to SMOTE on an average, they compromise on the Balanced accuracy of a classification model. LoRAS on the contrary, improves both F1 Score and the Balanced accuracy thus produces better classification models. Moreover, to explain the success of the algorithm, we have constructed a mathematical framework to prove that LoRAS oversampling technique provides a better estimate for the mean of the underlying local data distribution of the minority class data space.