Classification trees with unbiased multiway splits
Classification trees with unbiased multiway splits
复制标题
DOI：
10.1198/016214501753168271
复制
发表时间：
2001-06-01
影响因子：
3.7
通讯作者：
Loh, WY
中科院分区：
文献类型：
--
作者：
Kim, H;Loh, WY
提出了两种单变量分裂方法和一种线性组合分裂方法，用于构造具有多方向分裂的分类树。例子中给出的树更紧凑，因此更容易解释比二叉树。单变量拆分方法的一个主要优点是，当变量提供的拆分数量不同以及缺失值数量不同时，它们在变量选择中的偏差可以忽略不计。这是一个优点，因为从树结构的推断可能会受到选择偏差的不利影响。新的方法被证明是非常有竞争力的计算速度和分类精度的未来观测。
Two univariate split methods and one linear combination split method are proposed for the construction of classification trees with multiway splits. Examples are given where the trees are more compact and hence easier to interpret than binary trees. A major strength of the univariate split methods is that they have negligible bias in variable selection, both when the variables differ in the number of splits they offer and when they differ in the number of missing values. This is an advantage because inferences from the tree structures can be adversely affected by selection bias. The new methods are shown to be highly competitive in terms of computational speed and classification accuracy of future observations.