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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Assessing classifiers in terms of the partial area under the ROC curve
Computational Statistics and Data Analysis, Volume 64, Year 2013
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Description
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. © 2013 Published by Elsevier B.V.
Authors & Co-Authors
Yousef, Waleed A.
Egypt, Helwan
Faculty of Computers and Artificial Intelligence
Statistics
Citations: 23
Authors: 1
Affiliations: 1
Identifiers
Doi:
10.1016/j.csda.2013.02.032
ISSN:
01679473
Study Design
Cross Sectional Study