Maximization of AUC and Buffered AUC in binary classification
Maximization of AUC and Buffered AUC in binary classification
About this item
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Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
In binary classification, performance metrics that are defined as the probability that some error exceeds a threshold are numerically difficult to optimize directly and also hide potentially important information about the magnitude of errors larger than the threshold. Defining similar metrics, instead, using Buffered Probability of Exceedance (bPO...
Alternative Titles
Full title
Maximization of AUC and Buffered AUC in binary classification
Authors, Artists and Contributors
Author / Creator
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TN_cdi_proquest_journals_2064006504
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2064006504
Other Identifiers
ISSN
0025-5610
E-ISSN
1436-4646
DOI
10.1007/s10107-018-1312-2
