ZHANG Shangli, ZHANG Lili, QIU Kuanmin, et al., “Variable Selection in Logistic Regression Model,” Chinese Journal of Electronics, vol. 24, no. 4, pp. 813-817, 2015, doi: 10.1049/cje.2015.10.025
 Citation: ZHANG Shangli, ZHANG Lili, QIU Kuanmin, et al., “Variable Selection in Logistic Regression Model,” Chinese Journal of Electronics, vol. 24, no. 4, pp. 813-817, 2015,

# Variable Selection in Logistic Regression Model

##### doi: 10.1049/cje.2015.10.025
Funds:  This work is supported by the National Natural Science Foundation of China (No.61070236, No.U1334211), and the Project of State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University (No.RCS2012ZT004).
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• Corresponding author: ZHANG Lili (corresponding author)was born in Inner Mongolia, shereceived the B.E. degree in Mathematicsfrom Inner Mongolia University. She isnow a Ph.D. candidate of Chonnam NationalUniversity. Her research interests includepattern recognition and biostatistic.(Email: l1lzhang@126.com)
• Received Date: 2014-01-20
• Rev Recd Date: 2014-04-08
• Publish Date: 2015-10-10
• 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.
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