Genetic algorithm search for large logistic regression models with significant variables

被引：0

作者：

Stacey, A ^{[1
]}

Kildea, D ^{[1
]}

机构：

[1] Royal Melbourne Inst Technol, Dept Math, Melbourne, Vic 3000, Australia

来源：

ITI 2000: PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY INTERFACES | 2000年

关键词：

logistic regression; genetic algorithms; computational statistics;

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：

A Genetic Algorithm (GA) is described which searches the space of all possible subsets of predictor variables for the best Logistic Regression model containing only significant variables. The method has been shown to be effective on a data set with eighteen variables and on a larger data set of two hundred variables. For the smaller data set an exhaustive search I revealed only seven valid models, of which the GA found five. The method has been applied to Linear Regression with equal success. As GA's never guarantee to find an optimal solution the method is best described as an exploratory tool.

引用

页码：275 / 279

页数：5