摘要
模型和特征选择是统计学中较为重要的问题之一。Lasso是一种基于一范式的特征选择方法,与现有特征选择方法比较,Lasso不仅能够准确地选择出重要变量,同时还具有特征选择的稳定性。文中对线性回归模型中变量选择的Lasso算法、基于线性模型的Lasso、Lars、Adaptive-lasso、elastic net等方法进行了比较,指出了它们间的联系,并通过对几个选自UCI数据集的数据进行对比验证,给出了变量选择方法的具体实现。
The model and feature selection is one of the important subjects in statistics. Lasso is a feature selection method based on 1-norm. Compared with the existing features of selection methods, Lasso can not only accurately choose the important variables, but also has the stability of feature selection. This paper compares the Lasso algorithm of variable selection in linear regression model, and Lasso, Lars, the Adaptive Lasso, elastic net as well as other methods which are based on the linear model. The relationships between them are presented. A variable selection method is realized by the comparison tests of a few data selected from UCI.
出处
《安徽电子信息职业技术学院学报》
2014年第1期26-30,共5页
Journal of Anhui Vocational College of Electronics & Information Technology
基金
浙江省教育厅科研项目(Y201327368)
宁波市创新团队资助项目(2012B82002
2013B82005)
关键词
特征选择
Lasso算法
线性回归
变量选择
feature selection
Lasso algorithm
linear regression
variable selection
