Results 201 to 210 of about 304,364 (248)

The partial area under the summary ROC curve

Statistics in Medicine, 2005
The area under the curve (AUC) is commonly used as a summary measure of the receiver operating characteristic (ROC) curve. It indicates the overall performance of a diagnostic test in terms of its accuracy at various diagnostic thresholds used to discriminate cases and non-cases of disease.
Stephen Walter
exaly   +3 more sources

The Area under an ROC Curve with Limited Information

Medical Decision Making, 2003
The area under the receiver operating characteristic (ROC) curve of a diagnostic test can be used as a summary measure for its discriminative ability. If only a single point of an ROC curve is available, then the entire form of the ROC curve is unknown and the area under it cannot be calculated. Assuming that the unknown ROC curve is either monotone or
Wilbert B Van Den Hout
exaly   +3 more sources

On the limitations of the area under the ROC curve for NTCP modelling

Radiotherapy and Oncology, 2020
The area under the ROC curve (AUC) is commonly used as a measure for the discriminative performance of NTCP models. Here, we demonstrate that for typical patient cohorts, the AUC is an unsuitable measure for that purpose since it is typically limited to values below 0.8 and it exhibits large statistical variation.
Emanuel Bahn
exaly   +3 more sources

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

Computational Statistics and Data Analysis, 2013
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Waleed A Yousef
exaly   +3 more sources

Optimization of the Area under the ROC Curve

2008 10th Brazilian Symposium on Neural Networks, 2008
In this paper, we propose a new binary classification algorithm (AUCtron), based on gradient descent learning, that directly optimizes AUC (area under the ROC curve). We compare it with a linear classifier and with AUCsplit proposed. The AUCtron algorithm implicitly considers class prior probabilities in the decision criteria.
Cristiano Leite Castro, Antônio de Pádua Braga   +1 more
openaire   +1 more source

Estimation of the area under the ROC curve

Statistics in Medicine, 2002
AbstractThe area under the receiver operating characteristic curve is frequently used as a measure for the effectiveness of diagnostic markers. In this paper we discuss and compare estimation procedures for this area. These are based on (i) the Mann–Whitney statistic; (ii) kernel smoothing; (iii) normal assumptions; (iv) empirical transformations to ...
David, Faraggi, Benjamin, Reiser
openaire   +2 more sources

A : Extending area under the ROC curve for probabilistic labels

Pattern Recognition Letters, 2021
Abstract In ranking applications, AUCROC is widely used in measuring the performance of a discriminative model. But this is possible only if the labels are binary, like in { 0 , 1 } , as AUCROC is undefined for non-binary labels. In modelling applications where the labels are not from { 0 , 1 } but are probabilities of ...
Aditya Ramana Rachakonda, Ayush Bhatnagar   +1 more
openaire   +1 more source

Ranking Instances by Maximizing the Area under ROC Curve

IEEE Transactions on Knowledge and Data Engineering, 2013
In recent years, the problem of learning a real-valued function that induces a ranking over an instance space has gained importance in machine learning literature. Here, we propose a supervised algorithm that learns a ranking function, called ranking instances by maximizing the area under the ROC curve (RIMARC). Since the area under the ROC curve (AUC)
H. Altay Güvenir, Murat Kurtcephe
openaire   +3 more sources