Reinforced random forest

Angshuman Paul, D. Mukherjee
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引用次数: 12

Abstract

Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy. Our algorithm is initialized with a forest. Then the entire training data is tested using the initial forest. In order to reinforce learning, we use mis-classified data points to grow certain number of new trees. A subset of the new trees is added to the existing forest using a novel graph-based approach. We show that addition of these trees ensures improvement in classification accuracy. This process is continued iteratively until classification accuracy saturates. The proposed RRF has low computational burden. We achieve at least 3% improvement in F-measure compared to random forest in three breast cancer datasets. Results on benchmark datasets show significant reduction in average classification error.
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强化随机森林
强化学习提高了分类的准确性。但是在随机森林分类器的情况下,强化学习的使用是相对未被探索的。我们提出了一种利用强化学习来提高分类精度的增强随机森林(RRF)分类器。我们的算法是用森林初始化的。然后使用初始森林对整个训练数据进行测试。为了加强学习,我们使用错误分类的数据点来生长一定数量的新树。使用一种新颖的基于图的方法将新树的子集添加到现有的森林中。我们证明了这些树的添加确保了分类精度的提高。这个过程不断迭代,直到分类精度达到饱和。所提出的RRF具有较低的计算负担。在三个乳腺癌数据集中,与随机森林相比,我们的F-measure至少提高了3%。在基准数据集上的结果显示,平均分类误差显著降低。
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