Improving the diagnosis of breast cancer using regularized logistic regression with adaptive elastic net
Early diagnosis of breast cancer helps improve the patient's chance of survival. Therefore, cancer classification and feature selection are important research topics in medicine and biology. Recently, the adaptive elastic net was used effectively for feature-based cancer classification, allowin...
Saved in:
Main Authors: | Alharthi, Aiedh Mrisi, Lee, Muhammad Hisyam, Algama, Zakariya Yahya |
---|---|
格式: | Article |
语言: | English |
出版: |
Horizon Research Publishing
2021
|
主题: | |
在线阅读: | http://eprints.utm.my/id/eprint/95725/1/AiedhMrisi2021_ImprovingtheDiagnosisofBreastCancer.pdf http://eprints.utm.my/id/eprint/95725/ http://dx.doi.org/10.13189/ujph.2021.090514 |
标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
相似书籍
-
Embedded feature selection methods with high dimensionality for elastic net and logistic regression models
由: Alharthi, Aiedh Mrisi
出版: (2022) -
Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
由: Algamal, Zakariya Y., et al.
出版: (2015) -
Gene selection and classification of microarray gene expression data based on a new adaptive L1-norm elastic net penalty
由: Alharthi, Aiedh Mrisi, et al.
出版: (2021) -
Improving penalized logistic regression model with missing values in high-dimensional data
由: Alharthi, Aiedh Mrisi, et al.
出版: (2022) -
Weighted L1-norm logistic regression for gene selection of microarray gene expression classification
由: Alharthi, Aiedh Mrisi, et al.
出版: (2020)