Building Decision Tree for Imbalanced Classification via Deep Reinforcement Learning

Abstract

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.