نتایج جستجو برای: roc curves

تعداد نتایج: 107533  

Journal: :Electronic Commerce Research 2005
Andrew I. Schein Alexandrin Popescul Lyle H. Ungar David M. Pennock

Evaluation of a recommender system algorithm is a challenging task due to the many possible scenarios in which such systems may be deployed. We have designed a new performance plot called the CROC curve with an associated statistic: the area under the curve. Our CROC curve supplements the widely used ROC curve in recommender system evaluation by discovering performance characteristics that stan...

1999
C Andrieu JFG de Freitas

We present a novel and powerful strategy for estimating and combining classi ers via ROC curves, decision analysis theory and MCMC simulation. This paradigm also allows us to select samples from an MCMC run in a parsimonious and optimal fashion. Each ROC curve, corresponds to a sample (classi er) obtained with a full Bayesian model, which treats the model dimension, model parameters, regularisa...

Journal: :Clinical chemistry 1992
M H Zweig S K Broste R A Reinhart

Clinical accuracy, defined as the ability to discriminate between states of health, is the fundamental property of any diagnostic test or system. It is readily expressed as clinical sensitivity and specificity, and elegantly represented by the receiver operating characteristic (ROC) curve. To demonstrate the use of ROC curves, we reexamine a study of the ability of serum lipid and apolipoprotei...

2009
Deborah H Glueck Molly M Lamb Colin I O'Donnell Brandy M Ringham John T Brinton Keith E Muller John M Lewin Todd A Alonzo Etta D Pisano

BACKGROUND To compare the diagnostic accuracy of two continuous screening tests, a common approach is to test the difference between the areas under the receiver operating characteristic (ROC) curves. After study participants are screened with both screening tests, the disease status is determined as accurately as possible, either by an invasive, sensitive and specific secondary test, or by a l...

2007
Denis Robilliard Virginie Marion-Poty Sébastien Mahler Cyril Fonlupt

The so-called “boosting” principle was introduced by Schapire and Freund in the 1990s in relation to weak learners in the Probably Approximately Correct computational learning framework. Another practice that has developed in recent years consists in assessing the quality of evolutionary or genetic classifiers with Receiver Operating Characteristics (ROC) curves. Following the RankBoost algorit...

Most of the land use change modelers have used to model binary land use change rather than multiple land use changes. As a first objective of this study, we compared two well-known LUC models, called classification and regression tree (CART) and artificial neural network (ANN) from two groups of data mining tools, global parametric and local non-parametric models, to model multiple LUCs. The ca...

2014
Christos T. Nakas

• This article reviews current state of the art of ROC surface analysis and illustrates its use through an application on a pancreatic cancer diagnostic marker. Receiver Operating Characteristic (ROC) surfaces have been studied in the literature essentially only during the last decade and are considered as a natural generalization of ROC curves in three-class diagnostic problems. This article p...

Journal: :Bioinformatics 2014
Natalia Novoselova Cristina Della Beffa Junxi Wang Jialiang Li Frank Pessler Frank Klawonn

UNLABELLED Receiver operating characteristic (ROC) analysis is usually applied in bioinformatics to evaluate the abilities of biological markers to differentiate between the presence or absence of a disease. It includes the derivation of the useful scalar performance measure area under the ROC curve for binary classification tasks. As real applications often deal with more than two classes, mul...

2007
Marco Barreno Alvaro A. Cárdenas J. Doug Tygar

We present a new analysis for the combination of binary classifiers. Our analysis makes use of the Neyman-Pearson lemma as a theoretical basis to analyze combinations of classifiers. We give a method for finding the optimal decision rule for a combination of classifiers and prove that it has the optimal ROC curve. We show how our method generalizes and improves previous work on combining classi...

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