BORN AGAIN TREES
BORN AGAIN TREES
复制标题
DOI：
--
复制
发表时间：
1996
期刊：
影响因子：
--
通讯作者：
L. Breiman;Nong Shang
中科院分区：
文献类型：
--
作者：
L. Breiman;Nong Shang
树预测器（如CART或C4.5）通常不如神经网络或使用多棵树那样准确。但是后一种方法导致预测器的结构难以理解，而树具有普遍的简单性。正因为如此，试图找到更复杂预测器的树表示是很有吸引力的。我们研究多树预测的树表示。这些代表者比通常生长的树木更大，更稳定，更准确。因此，我们称它们为“重生树”。
Tree predictors such as CART or C4.5 are often not as accurate as neural nets or use of multiple trees. But these latter methods lead to predictors whose structure is difficult to understand, whereas trees have a universal simplicity. Because of this, it is appealing to try and find tree representations of more complex predictors. We study tree representers of multiple tree predictors. These representers are larger, more stable and more accurate than trees grown the usual way. For this reason, we call them "born again" trees.