A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms

A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms

Abstract Background In classification and diagnostic testing, the receiver-operator characteristic (ROC) plot and the area under the ROC curve (AUC) describe how an adjustable threshold causes changes in two types of error: false positives and false negatives. Only part of the ROC curve and AUC are...

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Bibliographic Details
Main Authors: André M. Carrington, Paul W. Fieguth, Hammad Qazi, Andreas Holzinger, Helen H. Chen, Franz Mayr, Douglas G. Manuel
Format: Article
Language:English
Published: BMC 2020-01-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Area under the ROC curve
Receiver operating characteristic
C statistic
Concordance
Partial area index
Imbalanced data
Online Access:https://doi.org/10.1186/s12911-019-1014-6

Internet

https://doi.org/10.1186/s12911-019-1014-6

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