Journal
APPLIED INTELLIGENCE
Volume 50, Issue 8, Pages 2488-2502Publisher
SPRINGER
DOI: 10.1007/s10489-020-01637-z
Keywords
Imbalanced classification; Deep reinforcement learning; Reward function; Classification policy
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available