Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine

DOI10.1016/j.jtbi.2018.12.010zbMath1406.92192OpenAlexW2903624772WikidataQ62492472 ScholiaQ62492472MaRDI QIDQ1717062

Erdal Karapınar

Publication date: 6 February 2019

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2018.12.010

zbMATH Keywords

gene expression datatumor classificationfeature genesgeneralized multi-class support vector machinerelaxed Lasso

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05) Learning and adaptive systems in artificial intelligence (68T05) Biochemistry, molecular biology (92C40)

Related Items (2)

SGL-SVM: a novel method for tumor classification via support vector machine with sparse group lasso ⋮ rL-GenSVM

Uses Software

• ROCR
• glmnet
• GenSVM
• GitHub
• LORIS

Cites Work

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• On the learnability and design of output codes for multiclass problems

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