Development of a Bayesian Framework for Determining Uncertainty in Receiver Operating Characteristic Curve Estimates

• Published in: IEEE transactions on knowledge and data engineering Vol. 22; no. 1; pp. 31 - 45
• Main Authors: Parker, D.R., Gustafson, S.C., Oxley, M.E., Ross, T.D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2010; IEEE Computer Society
• Subjects: Applied sciences; Bayesian methods; Biomedical imaging; Character generation; Computer science; control theory; systems; Data processing. List processing. Character string processing; Decision making; Exact sciences and technology; Focusing; Force sensors; Measurement; Memory organisation. Data processing; Object detection; Performance evaluation; performance metrics; receiver operating characteristic; Receivers; ROC curves; Software; target detection; Uncertainty; uncertainty estimation; Bayes estimation; Performance evaluation; Discriminant analysis; Modeling; ROC curves; uncertainty estimation; Probability density; Confidence; performance metrics; Receiver operating characteristic curves; Metric; receiver operating characteristic; Target detection

This research uses a Bayesian framework to develop probability densities for the receiver operating characteristic (ROC) curve. The ROC curve is a discrimination metric that may be used to quantify how well a detection system classifies targets and nontargets. The degree of uncertainty in ROC curve...