Extremely randomized trees
Extremely randomized trees
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DOI：
10.1007/s10994-006-6226-1
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发表时间：
2006-04-01
期刊：
影响因子：
7.5
通讯作者：
Wehenkel, L
中科院分区：
文献类型：
--
作者：
Geurts, P;Ernst, D;Wehenkel, L
本文提出了一种新的基于树的集成方法来解决监督分类和回归问题。它本质上是由随机强烈属性和切割点的选择，而分裂树节点。在极端情况下，它建立完全随机化的树，其结构与学习样本的输出值无关。随机化的强度可以通过适当选择参数来调整到问题的具体情况。我们评估这个参数的默认选择的鲁棒性，我们还提供了关于如何在特定情况下调整它的见解。除了准确性之外，所得到的算法的主要优点是计算效率。还提供了额外的树算法的偏差/方差分析，以及诱导模型的几何和内核表征。
This paper proposes anew tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The strength of the randomization can be tuned to problem specifics by the appropriate choice of a parameter. We evaluate the robustness of the default choice of this parameter, and we also provide insight on how to adjust it in particular situations. Besides accuracy, the main strength of the resulting algorithm is computational efficiency. A bias/variance analysis of the Extra-Trees algorithm is also provided as well as a geometrical and a kernel characterization of the models induced.