# Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data

arXiv: https://arxiv.org/abs/2401.02591 · HTML: https://mlsanity.com/paper/2401.02591/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 55 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists](https://mlsanity.com/paper/1710.04019/) (2017)
- [Discriminative Active Learning](https://mlsanity.com/paper/1907.06347/) (2019)
- [Deep Active Learning over the Long Tail](https://mlsanity.com/paper/1711.00941/) (2017)
- [Generative Adversarial Minority Oversampling](https://mlsanity.com/paper/1903.09730/) (2019)
- [DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data](https://mlsanity.com/paper/2105.02340/) (2021)

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

- [IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning](https://mlsanity.com/paper/2602.20199/) (2026)

## Abstract (arXiv)

This study examines the impact of class-imbalanced data on deep learning models and proposes a technique for data balancing by generating synthetic data for the minority class. Unlike random-based oversampling, our method prioritizes balancing the informative regions by identifying high entropy samples. Generating well-placed synthetic data can enhance machine learning algorithms accuracy and efficiency, whereas poorly-placed ones may lead to higher misclassification rates. We introduce an algorithm that maximizes the probability of generating a synthetic sample in the correct region of its class by optimizing the class posterior ratio. Additionally, to maintain data topology, synthetic data are generated within each minority sample's neighborhood. Our experimental results on forty-one datasets demonstrate the superior performance of our technique in enhancing deep-learning models.