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 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, in which we formulate the classification problem as a sequential decision-making process and solve it by a deep Q-learning network. In our model, the agent performs a classification action on one sample in each time step, and the environment evaluates the classification action and returns a reward to the agent. The reward from the 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 the specific reward function and beneficial simulated environment. Experiments have shown that our proposed model outperforms other imbalanced classification algorithms, and identifies more minority samples with better classification performance.

实际应用中的数据通常表现出偏斜的类分布，这对机器学习构成了严峻的挑战。常规分类算法在数据分布不平衡的情况下无效，并且在数据分布高度不平衡时可能会失败。为了解决这个问题，我们提出了一种基于深度强化学习的通用不平衡分类模型，该模型将分类问题表述为顺序决策过程，并通过深度Q学习网络对其进行求解。在我们的模型中，代理商在每个时间步中对一个样本执行分类操作，而环境会评估该分类操作并向代理商返回奖励。少数群体样本的报酬较大，因此代理对少数群体的敏感度更高。最终，代理在特定的奖励函数和有益的模拟环境的指导下，找到不平衡数据的最优分类策略。实验表明，我们提出的模型优于其他不平衡分类算法，并且可以识别出更多具有更好分类性能的少数样本。