ROC-based utility function maximization for feature selection and classification with applications to high-dimensional protease data

Biometrics

Volumn 64, Issue 4, 2008, Pages 1155-1161

  Liu, Zhenqiu ^a   Tan, Ming ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

Classification; LASSO; ROC; Sensitivity; Specificity; Utility maximization

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; ERROR STATISTICS; FEATURE SELECTION; LEARNING SYSTEMS; MATLAB;

DIFFERENT CLASS; FEATURE SELECTION AND CLASSIFICATION; HIGH-DIMENSIONAL; HIGHER-DIMENSIONAL; LASSO; ROC; SENSITIVITY; SPECIFICITY; UTILITY FUNCTION MAXIMIZATIONS; UTILITY MAXIMIZATIONS;

SITE SELECTION;

PROTEINASE;

ALGORITHM; CLASSIFICATION; COMPARATIVE STUDY; COST-BENEFIT ANALYSIS; DATA SET; OPTIMIZATION; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; SOFTWARE;

ALGORITHM; ARTICLE; COMPUTER PROGRAM; HUMAN IMMUNODEFICIENCY VIRUS 1; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY;

HUMAN IMMUNODEFICIENCY VIRUS 1;

EID: 56049088774     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2008.01015.x     Document Type: Article

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