Classification with tree-based ensembles applied to the WCCI 2006 Performance Prediction Challenge datasets

被引：0
|
作者：
Dahinden, Corinne [1 ]
机构：
[1] ETH, Seminar Stat, CH-8092 Zurich, Switzerland
来源：
2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10 | 2006年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Our contribution to the WCC1 2006 Performance Prediction Challenge is built on a modified Random Forests scheme, with cross-validation as a means for tuning parameters and estimating error-rates. This simple and computationally very efficient approach was found to yield better predictive performance than many algorithms of much higher complexity.
引用
收藏
页码：1669 / 1672
页数：4
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