论文标题
通过深厚的强化学习建立决策森林
Building Decision Forest via Deep Reinforcement Learning
论文作者
论文摘要
基本分类器是决策树的合奏学习方法通常属于行李或提升。但是，以前从未通过最大程度地提高长期回报来最大程度地提高整体分类器。本文提出了一种通过深度强化学习，一种称为MA-H-SAC-DF的决策森林建筑方法，用于二进制分类。首先，建筑过程被建模为分散的部分可观察的马尔可夫决策过程，一组合作社共同构建了所有基本分类器。其次，根据父节点和当前位置的信息来定义全球状态和局部观测值。最后，最先进的深钢筋方法混合囊扩展到CTDE架构下的多机构系统，以找到最佳的决策森林建筑政策。该实验表明，MA-H-SAC-DF的性能与平衡数据集上的随机森林，Adaboost和GBDT相同，并且在不平衡数据集上的表现优于它们。
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 jointly constructs all base classifiers. Second, the global state and local observations are defined based on informations of the parent node and the current location. Last, the state-of-the-art deep reinforcement method Hybrid SAC is extended to a multi-agent system under the CTDE architecture to find an optimal decision forest building policy. The experiments indicate that MA-H-SAC-DF has the same performance as random forest, Adaboost, and GBDT on balanced datasets and outperforms them on imbalanced datasets.
