# Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

arXiv: https://arxiv.org/abs/2504.05758 · HTML: https://mlsanity.com/paper/2504.05758/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion](https://mlsanity.com/paper/2502.03664/) (2025)
- [A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction](https://mlsanity.com/paper/2502.06847/) (2025)
- [A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning](https://mlsanity.com/paper/2502.09086/) (2025)
- [Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models](https://mlsanity.com/paper/2404.11606/) (2024)

## Built on by (most cited first, 9 of 9)

- [Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction](https://mlsanity.com/paper/2508.14942/) (2025)
- [Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks](https://mlsanity.com/paper/2505.08220/) (2025)
- [Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning](https://mlsanity.com/paper/2504.19583/) (2025)
- [Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions](https://mlsanity.com/paper/2504.15491/) (2025)
- [Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2505.05989/) (2025)
- [Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies](https://mlsanity.com/paper/2505.06145/) (2025)
- [Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems](https://mlsanity.com/paper/2505.16248/) (2025)
- [Integrating Feature Attention and Temporal Modeling for Collaborative Financial Risk Assessment](https://mlsanity.com/paper/2508.09399/) (2025)
- [Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning](https://mlsanity.com/paper/2505.00299/) (2025)

## Abstract (arXiv)

This study proposes a method for imbalanced data classification based on deep probabilistic graphical models (DPGMs) to solve the problem that traditional methods have insufficient learning ability for minority class samples. To address the classification bias caused by class imbalance, we introduce variational inference optimization probability modeling, which enables the model to adaptively adjust the representation ability of minority classes and combines the class-aware weight adjustment strategy to enhance the classifier's sensitivity to minority classes. In addition, we combine the adversarial learning mechanism to generate minority class samples in the latent space so that the model can better characterize the category boundary in the high-dimensional feature space. The experiment is evaluated on the Kaggle "Credit Card Fraud Detection" dataset and compared with a variety of advanced imbalanced classification methods (such as GAN-based sampling, BRF, XGBoost-Cost Sensitive, SAAD, HAN). The results show that the method in this study has achieved the best performance in AUC, Precision, Recall and F1-score indicators, effectively improving the recognition rate of minority classes and reducing the false alarm rate. This method can be widely used in imbalanced classification tasks such as financial fraud detection, medical diagnosis, and anomaly detection, providing a new solution for related research.