Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion

DOI10.1080/00949655.2014.899362zbMath1457.62215OpenAlexW2013148675MaRDI QIDQ5220892

Chenqun Yu, Jiaxu Chen, Ben-Chang Shia, Shuangge Ma, Danhui Yi, Yang Li, Yichen Qin, Li-Min Wang

Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2014.899362

zbMATH Keywords

group Lassoimbalanced datatrue positive ratecomposite criterion

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) 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)

Related Items (1)

Adaptive stochastic gradient boosting tree with composite criterion

Uses Software

• SMOTE

Cites Work

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• An asymmetric classifier based on partial least squares
• Cost-sensitive boosting for classification of imbalanced data
• Adaptive Weighted Learning for Unbalanced Multicategory Classification
• The Group Lasso for Logistic Regression
• A group bridge approach for variable selection
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• Model Selection and Estimation in Regression with Grouped Variables

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