Building Decision Tree for Imbalanced Classification via Deep Reinforcement Learning

Guixuan Wen (Chongqing University)*; Kaigui Wu (Chongqing University)


Data imbalance is prevalent in classification problems and tends to bias the classifier towards the majority of classes. This paper proposes a decision tree building method for imbalanced binary classification via deep reinforcement learning. First, the decision tree building process is regarded as a multi-step game and modeled as a Markov decision process. Then, the tree-based convolution is applied to extract state vectors from the tree structure, and each node is abstracted into a parameterized action. Next, the reward function is designed based on a range of evaluation metrics of imbalanced classification. Finally, a popular deep reinforcement learning algorithm called Multi-Pass DQN is employed to find an optimal decision tree building policy. The experiments on more than 15 imbalanced data sets indicate that our method outperforms the state-of-the-art methods.