Symbolic regression in multicollinearity problems

被引：0
|
作者：
Castillo, Flor A. [1 ]
Villa, Carlos M. [1 ]
机构：
[1] Dow Chem Co USA, Freeport, TX 77541 USA
来源：
GECCO 2005: Genetic and Evolutionary Computation Conference, Vols 1 and 2 | 2005年
关键词：
experimentation; multicollinearity; multiple regression; undesigned data;
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：
In this paper the potential of GP-generated symbolic regression for alleviating multicollinearity problems in multiple regression is presented with a case study in an industrial setting. The main advantage of this approach is the potential to produce a simple and stable polynomial model in terms of the original variables.
引用
收藏
页码：2207 / 2208
页数：2
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