Regression shrinkage and selection variables via an adaptive elastic net model

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 data...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-05, Vol.1879 (3), p.32014
Main Authors: Mahdi, Ghadeer Jasim Mohammed, Mohammed, Nadia Jasim, Al-Sharea, Zahraa Ibrahim
Format: Article
Language:English
Subjects:
Algorithms
Datasets
Feature selection
Leukemia
Physics
Variables
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1879/3/032014