难道有谁知道决策树分类中的任何增量实现。这样，它可以产生最佳的决策树分类当你添加新的实例来训练集低计算和尽可能快，根据现有的决策树分类？

Do anyone know any incremental implementation of decision tree classifier. Such that it could generate optimal decision tree classifier when you add new instance to training set with low computation and as quick as possible according existing decision tree classifier?

在换句话说，我已经设置了一个最佳的决策树分类的 A ，其中名为 T_1 ，现在我想添加的实例的 X 以将 A 并找到最佳的决策树分类树 T_2 按趁着 T_1 和 X 作为集 {A，X}

In other words I have an optimal decision tree classifier of set A, which named T_1, now I want to add instance X to set A and find optimal decision tree classifier tree T_2 by taking advantage of T_1 and X for set {A,X}.

添加实例将出现数次。因此，将有价值的给我找的，而不是每次都构建树增量法。

adding instances will occurs several times. So it will valuable for me to find incremental method instead of building the tree every time.

其实我很怀疑这样的实施是否存在。这是pciated如果有人可以帮助我AP $ P $。我preFER code C ++中如果有任何。

Actually I am suspicious whether such implementation exists or not. It is appreciated if anybody could help me. I prefer code in c++ if there is any.

感谢

推荐答案

在维基百科文章链接两个codeS。 ITI 是不开源的，但来源可采用，的 VMFL 是开源和C语言（主要是）

The wikipedia article links to two codes. ITI is not open source, but the source is avalable, VMFL is open source and in C (mostly)