Results 221 to 230 of about 304,364 (248)

Discrimination Index, the Area Under the ROC Curve

2002
The accuracy of fit of a mathematical predictive model is the degree to which the predicted values coincide with the observed outcome. When the outcome variable is dichotomous and predictions are stated as probabilities that an event will occur, models can be checked for good discrimination and calibration.
Byung-Ho Nam, Ralph B. D’Agostino
openaire   +1 more source

Optimising area under the ROC curve using gradient descent

Twenty-first international conference on Machine learning - ICML '04, 2004
This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC). Unlike standard binary classifiers, RankOpt adopts the AUC statistic as its objective function, and optimises it directly using gradient descent.
Alan Herschtal, Bhavani Raskutti
openaire   +1 more source

Linear model combining by optimizing the Area under the ROC curve

18th International Conference on Pattern Recognition (ICPR'06), 2006
In some classification problems, like the detection of illnesses in patients, classes are very unbalanced and the misclassification costs for different classes vary significantly. Then it is better not to minimize the classification error, but to optimize the ordering of the data, or to optimize the Area under the ROC curve (AUC).
David M. J. Tax, Robert P. W. Duin
openaire   +1 more source

Maximizing area under ROC curve for biometric scores fusion

Pattern Recognition, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kar-Ann Toh, Jaihie Kim, Sangyoun Lee
openaire   +2 more sources

The ROC Curve and the Area under It as Performance Measures

Weather and Forecasting, 2004
Abstract The receiver operating characteristic (ROC) curve is a two-dimensional measure of classification performance. The area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. In this short article, five idealized models are utilized to relate the shape of the ROC curve, and the area under it, to features ...
openaire   +1 more source

A Relationship between the Average Precision and the Area Under the ROC Curve

Proceedings of the 2015 International Conference on The Theory of Information Retrieval, 2015
For similar evaluation tasks, the area under the receiver operating characteristic curve (AUC) is often used by researchers in machine learning, whereas the average precision (AP) is used more often by the information retrieval community. We establish some results to explain why this is the case. Specifically, we show that, when both the AUC and the AP
Wanhua Su, Yan Yuan, Mu Zhu
openaire   +1 more source

Area Under the ROC Curve Maximization for Metric Learning

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022
Bojana Gajic, Ariel Amato, Ramon Baldrich, Joost van de Weijer 0001, Carlo Gatta   +4 more
openaire   +1 more source

Reflection on modern methods: Revisiting the area under the ROC Curve

International Journal of Epidemiology, 2020
A Cecile Janssens, Janssens A Cecile J W
exaly  

On the Reliability of the Area Under the ROC Curve in Empirical Software Engineering

Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, 2023
Luigi Lavazza, Sandro Morasca, Gabriele Rotoloni   +2 more
openaire   +1 more source

The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition, 1997
Andrew P Bradley
exaly