稳健性(进化)
数学
人工智能
算法
核(代数)
决策树
计算机科学
机器学习
树(集合论)
模式识别(心理学)
生物化学
基因
组合数学
数学分析
化学
作者
Pierre Geurts,Damien Ernst,Louis Wehenkel
出处
期刊:Machine Learning
[Springer Nature]
日期:2006-03-02
卷期号:63 (1): 3-42
被引量:5028
标识
DOI:10.1007/s10994-006-6226-1
摘要
This paper proposes a new 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.
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