Abstract. Receiver Operating Characteristic (ROC) analysis has been successfully applied to classifier problems with two classes. The Area Under the ROC Curve (AUC) has been elected as a better way to evaluate classifiers than predictive accuracy or error and has also recently used for evaluating probability estimators. However, the extension of the Area Under the ROC Curve for more than two classes has not been addressed to date, because of the complexity and elusiveness of its precise definition. Some approximations to the real AUC are used without an exact appraisal of their quality. In this paper, we present the real extension to the Area Under the ROC Curve in the form of the Volume Under the ROC Surface (VUS), showing how to compute t...
The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of c...
Statistical models are commonly used to predict the outcome of events in a wide variety of fields su...
In this paper, we propose a method for the linear combination of several dichotomizers aimed at maxi...
Receiver Operating Characteristic (ROC) has been successfully applied to classifier problems with tw...
The Receiver Operating Characteristic (ROC) has become a standard tool for the analysis and compari...
Within the last two decades, Receiver Operating Characteristic (ROC) Curves have become a standard t...
The performance of a classifier can be improved by abstaining on uncertain instance classifications....
Abstract—Receiver operator characteristic (ROC) analysis has become a standard tool in the design an...
The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of c...
Multiclass receiver operating characteristic (ROC) analysis has remained an open theoretical problem...
The majority of the available classification systems focus on the minimization of the classification...
In recent years, classifier combination has been of great interest for the pattern recognition commu...
The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification proble...
The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of c...
Statistical models are commonly used to predict the outcome of events in a wide variety of fields su...
In this paper, we propose a method for the linear combination of several dichotomizers aimed at maxi...
Receiver Operating Characteristic (ROC) has been successfully applied to classifier problems with tw...
The Receiver Operating Characteristic (ROC) has become a standard tool for the analysis and compari...
Within the last two decades, Receiver Operating Characteristic (ROC) Curves have become a standard t...
The performance of a classifier can be improved by abstaining on uncertain instance classifications....
Abstract—Receiver operator characteristic (ROC) analysis has become a standard tool in the design an...
The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of c...
Multiclass receiver operating characteristic (ROC) analysis has remained an open theoretical problem...
The majority of the available classification systems focus on the minimization of the classification...
In recent years, classifier combination has been of great interest for the pattern recognition commu...
The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification proble...
The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of c...
Statistical models are commonly used to predict the outcome of events in a wide variety of fields su...
In this paper, we propose a method for the linear combination of several dichotomizers aimed at maxi...