  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.
