Open AccessRegression shrinkage and selection variables via an adaptive elastic net model
Author(s) -
Ghadeer Jasim Mohammed Mahdi,
Nadia Jasim Mohammed,
Zahraa Ibrahim Al-Sharea
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1879/3/032014
Subject(s) - elastic net regularization , selection (genetic algorithm) , feature selection , computer science , variable (mathematics) , shrinkage , model selection , regression analysis , net (polyhedron) , variables , regression , data mining , artificial intelligence , machine learning , mathematics , statistics , mathematical analysis , geometry
In this paper, a new method of selection variables is presented to select some essential variables from large datasets. The new model is a modified version of the Elastic Net model. The modified Elastic Net variable selection model has been summarized in an algorithm. It is applied for Leukemia dataset that has 3051 variables (genes) and 72 samples. In reality, working with this kind of dataset is not accessible due to its large size. The modified model is compared to some standard variable selection methods. Perfect classification is achieved by applying the modified Elastic Net model because it has the best performance. All the calculations that have been done for this paper are in R program by using some existing packages.