Comparison of Non-Parametric Methods for Assessing Classifier Performance in Terms of ROC Parameters
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
ROC confidence bands: an empirical evaluation
ICML '05 Proceedings of the 22nd international conference on Machine learning
Estimating the uncertainty in the estimated mean area under the ROC curve of a classifier
Pattern Recognition Letters
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Assessing Classifiers from Two Independent Data Sets Using ROC Analysis: A Nonparametric Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric estimation of the threshold at an operating point on the ROC curve
Computational Statistics & Data Analysis
Confidence interval estimation of partial area under curve based on combined biomarkers
Computational Statistics & Data Analysis
Classifier variability: Accounting for training and testing
Pattern Recognition
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Assessing classifiers using the partial area under the ROC curve (PAUC) (or its equivalent, ''separability'', that is a function of the chosen threshold of the decision variable) is considered. The population properties of the ''separability'' as a function only of the trained classifier and the selected threshold are derived. Next, the nonparametric estimation of the ''separability'' and its mean, for which we assume the availability of only one dataset, using the leave-pair-out bootstrap-based estimator is considered. In addition, the influence function approach to estimate the uncertainty of that estimate is used. The major contributions are the inclusion of the effect of the training set on the properties of the ''separability'', and also on its nonparametric estimator, in both the mean and the variance; this is a key difference from the PAUC literature and its use in medical community. The mathematical properties are confirmed by a set of experiments using simulated and real datasets. Finally, the true performance (not its estimate) of classifiers measured in ''separability'' may vary significantly with varying the training set, while its estimate yet has a small estimated variance. This accounts for having ''good'' estimate for ''bad'' performance.