Results 11 to 20 of about 127,975,471 (298)

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. [PDF]

Electron J Stat, 2015
LeDell E, Petersen M, van der Laan M.
europepmc   +2 more sources

The area under the ROC curve as a measure of clustering quality

Data Mining and Knowledge Discovery, 2022
The Area Under the the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers.
Pablo A. Jaskowiak, Ivan G. Costa, Ricardo J. G. B. Campello   +2 more
openaire   +2 more sources

Estimating the optimal linear combination of predictors using spherically constrained optimization

BMC Bioinformatics, 2022
Background In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing the area under the receiver operating characteristic curve.
Priyam Das, Debsurya De, Raju Maiti, Mona Kamal, Katherine A. Hutcheson, Clifton D. Fuller, Bibhas Chakraborty, Christine B. Peterson   +7 more
doaj   +1 more source

Exact Probability Distribution for the ROC Area Under Curve

Cancers, 2022
Summary The Receiver Operating Characteristic (ROC) is a de facto standard for determining the accuracy of in vitro diagnostic (IVD) medical devices, and thus exactness in its probability distribution is crucial toward accurate statistical ...
Joakim Ekström, Jim Åkerrén Ögren, Tobias Sjöblom   +2 more
openaire   +3 more sources

Reliability of Systematic and Targeted Biopsies versus Prostatectomy

Bioengineering, 2023
Systematic Biopsy (SBx) has been and continues to be the standard staple for detecting prostate cancer. The more expensive MRI guided biopsy (MRITBx) is a better way of detecting cancer. The prostatectomy can provide an accurate condition of the prostate.
Tianyuan Guan, Abhinav Sidana, Marepalli B. Rao   +2 more
doaj   +1 more source

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

BioData Mining, 2023
Binary classification is a common task for which machine learning and computational statistics are used, and the area under the receiver operating characteristic curve (ROC AUC) has become the common standard metric to evaluate binary classifications in ...
Davide Chicco, Giuseppe Jurman
doaj   +1 more source

Simple tuberculosis screening tool using signs, symptoms, and risk factors to reduce the missed opportunity in the older population

BMC Pulmonary Medicine, 2022
Background There is a growing concern on how to increase tuberculosis (TB) case detection in resource-poor settings. The healthcare facilities routinely providing services to the elderly for chronic diseases often failed to detect TB cases, causing a ...
Agus Hidayat, Bhisma Murti, Soedarsono Soedarsono, Harsini, Chatarina Umbul Wahyuni, Isna Qodrijati   +5 more
doaj   +1 more source

ImbTreeAUC: An R package for building classification trees using the area under the ROC curve (AUC) on imbalanced datasets

SoftwareX, 2021
In this paper, we propose a novel R package, named ImbTreeAUC, for building binary and multiclass decision tree using the area under the receiver operating characteristic (ROC) curve.
Krzysztof Gajowniczek, Tomasz Ząbkowski
doaj   +1 more source

A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification

Algorithms, 2021
This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific ...
Umberto Michelucci, Michela Sperti, Dario Piga, Francesca Venturini, Marco A. Deriu   +4 more
doaj   +1 more source

AUC optimization for deep learning-based voice activity detection

EURASIP Journal on Audio, Speech, and Music Processing, 2022
Voice activity detection (VAD) based on deep neural networks (DNN) have demonstrated good performance in adverse acoustic environments. Current DNN-based VAD optimizes a surrogate function, e.g., minimum cross-entropy or minimum squared error, at a given
Xiao-Lei Zhang, Menglong Xu
doaj   +1 more source