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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
SDM
2008
SIAM
157views Data Mining» more  SDM 2008»
13 years 6 months ago
ROC-tree: A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data
Gene expression information from microarray experiments is a primary form of data for biological analysis and can offer insights into disease processes and cellular behaviour. Suc...
M. Maruf Hossain, Md. Rafiul Hassan, James Bailey
ICPR
2010
IEEE
13 years 4 months ago
The Binormal Assumption on Precision-Recall Curves
—The precision-recall curve (PRC) has become a widespread conceptual basis for assessing classification performance. The curve relates the positive predictive value of a classi...
Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno ...
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
2008
IEEE
14 years 5 months ago
Variance estimation for two-class and multi-class ROC analysis using operating point averaging
Receiver Operating Characteristic (ROC) analysis enables fine-tuning of a trained classifier to a desired performance trade-off situation. ROC estimated from a finite test set is,...
Pavel Paclík, Carmen Lai, Jana Novovicov&aa...