Open AccessVariable Selection in Logistic Regression Model
Author(s) -
Zhang Shangli,
Zhang Lili,
Qiu Kuanmin,
Lu Ying,
Cai Baigen
Publication year - 2015
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2015.10.025
Subject(s) - lasso (programming language) , logistic regression , feature selection , elastic net regularization , selection (genetic algorithm) , variable (mathematics) , mathematics , statistics , logistic model tree , linear regression , regression analysis , computer science , mathematical optimization , artificial intelligence , world wide web , mathematical analysis
Variable selection is one of the most important problems in pattern recognition. In linear regression model, there are many methods can solve this problem, such as Least absolute shrinkage and selection operator (LASSO) and many improved LASSO methods, but there are few variable selection methods in generalized linear models. We study the variable selection problem in logistic regression model. We propose a new variable selection method‐the logistic elastic net, prove that it has grouping effect which means that the strongly correlated predictors tend to be in or out of the model together. The logistic elastic net is particularly useful when the number of predictors ( p ) is much bigger than the number of observations ( n ). By contrast, the LASSO is not a very satisfactory variable selection method in the case when p is more larger than n . The advantage and effectiveness of this method are demonstrated by real leukemia data and a simulation study.