Building Decision Forest via Deep Reinforcement Learning

@article{Wen2022BuildingDF,
  title={Building Decision Forest via Deep Reinforcement Learning},
  author={Guixuan Wen and Kaigui Wu},
  journal={ArXiv},
  year={2022},
  volume={abs/2204.00306}
}
Ensemble learning methods whose base classifier is a decision tree usually belong to the bagging or boosting. However, no previous work has ever built the ensemble classifier by maximizing long-term returns to the best of our knowledge. This paper proposes a decision forest building method called MA-H-SAC-DF for binary classification via deep reinforcement learning. First, the building process is modeled as a decentralized partial observable Markov decision process, and a set of cooperative agents… 

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