# Clustering and Learning from Imbalanced Data

arXiv: https://arxiv.org/abs/1811.00972 · HTML: https://mlsanity.com/paper/1811.00972/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2018.

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

- [Predicate correlation learning for scene graph generation](https://mlsanity.com/paper/2107.02713/) (2021)
- [Quantum Machine Learning for Finance](https://mlsanity.com/paper/2109.04298/) (2021)
- [Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition](https://mlsanity.com/paper/2209.02960/) (2022)
- [Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss](https://mlsanity.com/paper/2211.10906/) (2022)
- [Covariance-corrected Whitening Alleviates Network Degeneration on Imbalanced Classification](https://mlsanity.com/paper/2408.17197/) (2024)
- [You Only Need End-to-End Training for Long-Tailed Recognition](https://mlsanity.com/paper/2112.05958/) (2021)
- [Optimizing Class Distributions for Bias-Aware Multi-Class Learning](https://mlsanity.com/paper/2509.11588/) (2025)

## Abstract (arXiv)

A learning classifier must outperform a trivial solution, in case of imbalanced data, this condition usually does not hold true. To overcome this problem, we propose a novel data level resampling method - Clustering Based Oversampling for improved learning from class imbalanced datasets. The essential idea behind the proposed method is to use the distance between a minority class sample and its respective cluster centroid to infer the number of new sample points to be generated for that minority class sample. The proposed algorithm has very less dependence on the technique used for finding cluster centroids and does not effect the majority class learning in any way. It also improves learning from imbalanced data by incorporating the distribution structure of minority class samples in generation of new data samples. The newly generated minority class data is handled in a way as to prevent outlier production and overfitting. Implementation analysis on different datasets using deep neural networks as the learning classifier shows the effectiveness of this method as compared to other synthetic data resampling techniques across several evaluation metrics.