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GECCO
2006
Springer
173views Optimization» more  GECCO 2006»
13 years 8 months ago
Sets of receiver operating characteristic curves and their use in the evaluation of multi-class classification
Within the last two decades, Receiver Operating Characteristic (ROC) Curves have become a standard tool for the analysis and comparison of classifiers since they provide a conveni...
Stephan M. Winkler, Michael Affenzeller, Stefan Wa...
ICPR
2004
IEEE
14 years 5 months ago
Sample Size Estimation using the Receiver Operating Characteristic Curve
In this paper we describe two related approaches to estimating the sample sizes required to statistically compare the performance of two classifiers: acceptable failure rates (AFR...
Andrew P. Bradley, I. Dennis Longstaff
JEI
2008
128views more  JEI 2008»
13 years 4 months ago
Practical use of receiver operating characteristic analysis to assess the performances of defect detection algorithms
Defect detection in images is a current task in quality control and is often integrated in partially or fully automated systems. Assessing the performances of defect detection algo...
Yann Le Meur, Jean-Michel Vignolle, Jocelyn Chanus...
ICML
2003
IEEE
14 years 5 months ago
Regression Error Characteristic Curves
Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generaliz...
Jinbo Bi, Kristin P. Bennett
ICPR
2006
IEEE
14 years 5 months ago
Precision-recall operating characteristic (P-ROC) curves in imprecise environments
Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this pa...
Thomas Landgrebe, Pavel Paclík, Robert P. W...