Deep Reinforcement Learning for Imbalanced Classification

2019-01-05 · Enlu Lin, Qiong Chen, Xiaoming Qi

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.

📄 PDF Abstract BibTeX arXiv:1901.01379

Code (3)

linenus/DRL-For-imbalanced-Classification 공식 구현 tf

Denbergvanthijs/imbDRL tf

Montherapy/Deep-reinforcement-learning-for-multi-class-imbalanced-classification tf

Tasks

Classification Decision MakingDeep Reinforcement LearningGeneral Classificationimbalanced classificationQ-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Sequential Decision Making

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