A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification

DOI10.1007/s11634-018-0334-1zbMath1474.62273OpenAlexW2886244096MaRDI QIDQ2324259

Muhammad Hisyam Lee, Zakariya Yahya Algamal

Publication date: 11 September 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-018-0334-1

zbMATH Keywords

LassoSCADgene selectioncancer classificationsparse logistic regression

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40)

Related Items

Nested cross-validation with ensemble feature selection and classification model for high-dimensional biological data ⋮ Adaptive sparse group LASSO in quantile regression

Uses Software

• glmnet
• SparseLOGREG

Cites Work

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