Optimizing area under the ROC curve using semi-supervised learning

DOI10.1016/J.PATCOG.2014.07.025zbMath1373.68337OpenAlexW2055630880WikidataQ34482907 ScholiaQ34482907MaRDI QIDQ1677038

Diana Li, Nicholas Petrick, Berkman Sahiner, Ronald M. Summers, Marius George Linguraru, Shijun Wang

Publication date: 10 November 2017

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4226543

zbMATH Keywords

semidefinite programmingreceiver operating characteristicsemi-supervised learningtransfer learningaucRankBoostSSLROCSVMROC

Mathematics Subject Classification ID

Semidefinite programming (90C22) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Neighborhood hypergraph based classification algorithm for incomplete information system

Uses Software

• SDPT3
• UCI-ml
• YALMIP
• LIBSVM
• AdaBoost.MH
• HandTill2001
• SVM
• SeDuMi Interface

Cites Work

• Unnamed Item
• Unnamed Item
• Bagging predictors
• Solving semidefinite-quadratic-linear programs using SDPT3
• One-pass AUC optimization
• Maximizing area under ROC curve for biometric scores fusion
• A critical analysis of variants of the AUC
• Maximizing the area under the ROC curve by pairwise feature combination
• A sharper Bonferroni procedure for multiple tests of significance
• 10.1162/1532443041827916
• Semidefinite Programming
• A simple generalisation of the area under the ROC curve for multiple class classification problems
• SeDuMi Interface

This page was built for publication: Optimizing area under the ROC curve using semi-supervised learning