# A Bilevel Optimization Framework for Imbalanced Data Classification

arXiv: https://arxiv.org/abs/2410.11171 · HTML: https://mlsanity.com/paper/2410.11171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters](https://mlsanity.com/paper/1511.06727/) (2015)
- [Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization](https://mlsanity.com/paper/2006.15766/) (2020)
- [AutoBalance: Optimized Loss Functions for Imbalanced Data](https://mlsanity.com/paper/2201.01212/) (2022)
- [Challenges of Big Data Analysis](https://mlsanity.com/paper/1308.1479/) (2013)
- [Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection](https://mlsanity.com/paper/2104.05702/) (2021)
- [Theory-Inspired Path-Regularized Differential Network Architecture Search](https://mlsanity.com/paper/2006.16537/) (2020)
- [SMOTified-GAN for class imbalanced pattern classification problems](https://mlsanity.com/paper/2108.03235/) (2021)
- [Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise](https://mlsanity.com/paper/2312.07087/) (2023)
- [Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization](https://mlsanity.com/paper/2204.10231/) (2022)

## Built on by (most cited first, 1 of 1)

- [Sampling Imbalanced Data with Multi-objective Bilevel Optimization](https://mlsanity.com/paper/2506.11315/) (2025)

## Abstract (arXiv)

Data rebalancing techniques, including oversampling and undersampling, are a common approach to addressing the challenges of imbalanced data. To tackle unresolved problems related to both oversampling and undersampling, we propose a new undersampling approach that: (i) avoids the pitfalls of noise and overlap caused by synthetic data and (ii) avoids the pitfall of under-fitting caused by random undersampling. Instead of undersampling majority data randomly, our method undersamples datapoints based on their ability to improve model loss. Using improved model loss as a proxy measurement for classification performance, our technique assesses a datapoint's impact on loss and rejects those unable to improve it. In so doing, our approach rejects majority datapoints redundant to datapoints already accepted and, thereby, finds an optimal subset of majority training data for classification. The accept/reject component of our algorithm is motivated by a bilevel optimization problem uniquely formulated to identify the optimal training set we seek. Experimental results show our proposed technique with F1 scores up to 10% higher than state-of-the-art methods.