# Deep Reinforcement Learning for Imbalanced Classification

arXiv: https://arxiv.org/abs/1901.01379 · HTML: https://mlsanity.com/paper/1901.01379/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2019.

## Built on by (most cited first, 24 of 24)

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- [Generating GPU Compiler Heuristics using Reinforcement Learning](https://mlsanity.com/paper/2111.12055/) (2021)
- [Application of Deep Reinforcement Learning to Payment Fraud](https://mlsanity.com/paper/2112.04236/) (2021)
- [Deep learning for understanding multilabel imbalanced Chest X-ray datasets](https://mlsanity.com/paper/2207.14408/) (2022)
- [GroupFace: Imbalanced Age Estimation Based on Multi-hop Attention Graph Convolutional Network and Group-aware Margin Optimization](https://mlsanity.com/paper/2412.11450/) (2024)
- [Deep Reinforcement Learning for Multi-class Imbalanced Training](https://mlsanity.com/paper/2205.12070/) (2022)
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- [Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error](https://mlsanity.com/paper/2212.13175/) (2022)
- [A Review of Machine Learning Techniques in Imbalanced Data and Future Trends](https://mlsanity.com/paper/2310.07917/) (2023)
- [An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models](https://mlsanity.com/paper/2404.15518/) (2024)
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- [LRA-GNN: Latent Relation-Aware Graph Neural Network with Initial and Dynamic Residual for Facial Age Estimation](https://mlsanity.com/paper/2502.05423/) (2025)
- [Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification](https://mlsanity.com/paper/2509.19694/) (2025)
- [Reinforcement Learning for Micro-Level Claims Reserving](https://mlsanity.com/paper/2601.07637/) (2026)
- [Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark](https://mlsanity.com/paper/2205.10441/) (2022)
- [Reinforced Pedestrian Attribute Recognition with Group Optimization Reward](https://mlsanity.com/paper/2205.14042/) (2022)
- [A Novel Plagiarism Detection Approach Combining BERT-based Word Embedding, Attention-based LSTMs and an Improved Differential Evolution Algorithm](https://mlsanity.com/paper/2305.02374/) (2023)
- [Scope Loss for Imbalanced Classification and RL Exploration](https://mlsanity.com/paper/2308.04024/) (2023)
- [Balancing the Scales: Reinforcement Learning for Fair Classification](https://mlsanity.com/paper/2407.10629/) (2024)
- [The Answer Lies Within: Self-Derived Rewards Enable Explainable Relation Extraction](https://mlsanity.com/paper/2510.06198/) (2025)
- [Judging by the Rules: Compliance-Aligned Framework for Modern Slavery Statement Monitoring](https://mlsanity.com/paper/2511.07803/) (2025)

## Abstract (arXiv)

Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in the case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced. To address this issue, we propose a general imbalanced classification model based on deep reinforcement learning. We formulate the classification problem as a sequential decision-making process and solve it by deep Q-learning network. The agent performs a classification action on one sample at each time step, and the environment evaluates the classification action and returns a reward to the agent. The reward from minority class sample is larger so the agent is more sensitive to the minority class. The agent finally finds an optimal classification policy in imbalanced data under the guidance of specific reward function and beneficial learning environment. Experiments show that our proposed model outperforms the other imbalanced classification algorithms, and it can identify more minority samples and has great classification performance.