模型决策树：一种决策树加速算法

Model Decision Tree: An Accelerated Algorithm of Decision Tree

决策树算法采用递归方法构建,训练效率较低,过度分类的决策树可能产生过拟合现象.因此,文中提出模型决策树算法.首先在训练数据集上采用基尼指数递归生成一棵不完全决策树,然后使用一个简单分类模型对其中的非纯伪叶结点(非叶结点且结点包含的样本不属于同一类)进行分类,生成最终的决策树.相比原始的决策树算法,这样产生的模型决策树能在算法精度不损失或损失很小的情况下,提高决策树的训练效率.在标准数据集上的实验表明,文中提出的模型决策树在速度上明显优于决策树算法,具备一定的抗过拟合能力.

The decision tree algorithm is constructed in a recursive style. Therefore, the low training efficiency is yielded and the over-classification of decision tree may produce overfitting. An accelerated algorithm called model decision tree （MDT） is proposed in this paper. An incomplete classification decision tree is established via the Gini index on the training dataset firstly. Then a simple model is utilized to classify impure pseudo leaf nodes, which are neither leaf nodes nor in the same class. Consequently, the final MDT is generated. Compared with DT, MDT improves the training efficiency with smaller loss of classification accuracy or even no loss. The experimental results on benchmark datasets show that the proposed MDT is much faster than DT and it has a certain ability to avoid overfitting.