Feature Selection and Cancer Classification via Sparse Logistic Regression with the Hybrid L(1/2 +2) Regularization

Cancer classification and feature (gene) selection plays an important role in knowledge discovery in genomic data. Although logistic regression is one of the most popular classification methods, it does not induce feature selection. In this paper, we presented a new hybrid L(1/2 +2) regularization (...

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Detalles Bibliográficos
Autores principales: Huang, Hai-Hui, Liu, Xiao-Ying, Liang, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4852916/
https://www.ncbi.nlm.nih.gov/pubmed/27136190
http://dx.doi.org/10.1371/journal.pone.0149675

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4852916/
https://www.ncbi.nlm.nih.gov/pubmed/27136190
http://dx.doi.org/10.1371/journal.pone.0149675

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