Assessing classifiers in terms of the partial area under the ROC curve

DOI10.1016/J.CSDA.2013.02.032zbMath1468.62223OpenAlexW2013921804MaRDI QIDQ1800075

Waleed A. Yousef

Publication date: 19 October 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.02.032

zbMATH Keywords

classificationbootstrapnonparametric inferencepartial area under the ROC curveclassifier assessment

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric statistical resampling methods (62G09)

Related Items (3)

Making classifier performance comparisons when ROC curves intersect ⋮ On the optimal binary classifier with an application ⋮ A criterion for the comparison of binary classifiers based on a stochastic dominance with an application to the sale of home insurances

Uses Software

• UCI-ml
• ElemStatLearn
• bootstrap

Cites Work

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• Classifier variability: accounting for training and testing
• Nonparametric estimation of the threshold at an operating point on the ROC curve
• Confidence interval estimation of partial area under curve based on combined biomarkers
• Partial AUC Estimation and Regression
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• A Probability Distribution and Its Uses in Fitting Data
• Improvements on Cross-Validation: The .632+ Bootstrap Method
• The elements of statistical learning. Data mining, inference, and prediction

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