Robust Sparse Logistic Regression With the <inline-formula> <tex-math notation="LaTeX">$L_{q}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$0 &lt; \text{q} &lt; 1$ </tex-math></inline-formula>) Regularization for Feature Selection Using Gene Expression Data

Robust Sparse Logistic Regression With the <inline-formula> <tex-math notation="LaTeX">$L_{q}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$0 &lt; \text{q} &lt; 1$ </tex-math></inline-formula>) Regularization for Feature Selection Using Gene Expression Data

Microarray technology is a popular technique that has been extensively applied in cancer diagnosis. Many studies have used high-dimensional microarray data to identify informative features to classify the types of cancer, yet numerous irrelevant features that exist in microarray data may introduce t...

Full description

Bibliographic Details
Main Authors: Ziyi Yang, Yong Liang, Hui Zhang, Hua Chai, Bowen Zhang, Cheng Peng
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Robust logistic regression
feature selection
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L</italic>q regularization
genetic algorithm
Online Access:https://ieeexplore.ieee.org/document/8528834/

Internet

https://ieeexplore.ieee.org/document/8528834/

Similar Items