论文标题
带有部分随机决策树的梯度提升机
Gradient boosting machine with partially randomized decision trees
论文作者
论文摘要
梯度提升机是一种强大的基于整体的机器学习方法，用于解决回归问题。但是，其使用的困难之一是回归函数可能的不连续性，这是当训练点不被训练点密集覆盖的区域时会产生。为了克服这一难度并降低梯度提升机的计算复杂性，我们建议将部分随机的树应用于施加到梯度增强的极端随机树的特殊情况。使用合成和真实数据的许多数值示例来说明带有部分随机树的梯度提升机。
The gradient boosting machine is a powerful ensemble-based machine learning method for solving regression problems. However, one of the difficulties of its using is a possible discontinuity of the regression function, which arises when regions of training data are not densely covered by training points. In order to overcome this difficulty and to reduce the computational complexity of the gradient boosting machine, we propose to apply the partially randomized trees which can be regarded as a special case of the extremely randomized trees applied to the gradient boosting. The gradient boosting machine with the partially randomized trees is illustrated by means of many numerical examples using synthetic and real data.
