Optimal dyadic decision trees

被引：26

作者：

Blanchard, G.

Schaefer, C.

Rozenholc, Y.

Mueller, K. -R.

机构：

[1] Fraunhofer First IDA, D-12489 Berlin, Germany

[2] Univ Paris 05, Appl Math Dept MAP5, F-75270 Paris, France

[3] Tech Univ Berlin, Dept Comp Sci, D-10587 Berlin, Germany

来源：

MACHINE LEARNING | 2007年 / 66卷 / 2-3期

关键词：

decision tree; oracle inequality; adaptive convergence rate; classification; density estimation;

D O I：

10.1007/s10994-007-0717-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce a new algorithm building an optimal dyadic decision tree (ODT). The method combines guaranteed performance in the learning theoretical sense and optimal search from the algorithmic point of view. Furthermore it inherits the explanatory power of tree approaches, while improving performance over classical approaches such as CART/C4.5, as shown on experiments on artificial and benchmark data.

引用

页码：209 / 241

页数：33

共 22 条

[1]

ADELSONVELSKII GM, 1962, DOKL AKAD NAUK SSSR+, V146, P263

[2] Risk bounds for model selection via penalization [J].