Results 21 to 30 of about 3,494,638 (328)
A Bayesian Hierarchical Model for Comparing Average F1 Scores [PDF]
2015 IEEE International Conference on Data Mining, 2015 In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data.
Zhang, Dell +3 more
openaire +1 more source
Diagnostics, 2022 Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease.
Mardhiyati Mohd Yunus +8 more
doaj +1 more source
Data augmentation and semi-supervised learning for deep neural networks-based text classifier [PDF]
, 2020 User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier.
Devlin Jacob +5 more
core +1 more source
Automated Drone Detection Using YOLOv4
Drones, 2021 Drones are increasing in popularity and are reaching the public faster than ever before. Consequently, the chances of a drone being misused are multiplying.
Subroto Singha, Burchan Aydin
doaj +1 more source
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining [PDF]
Briefings Bioinform., 2022 Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.
Renqian Luo +6 more
semanticscholar +1 more source
Confidence interval for micro-averaged F1 and macro-averaged F1 scores
Applied intelligence (Boston), 2021 A binary classification problem is common in medical field, and we often use sensitivity, specificity, accuracy, negative and positive predictive values as measures of performance of a binary predictor.
Kanae Takahashi +3 more
semanticscholar +1 more source
Benchmarking Low-Frequency Variant Calling With Long-Read Data on Mitochondrial DNA
Frontiers in Genetics, 2022 Background: Sequencing quality has improved over the last decade for long-reads, allowing for more accurate detection of somatic low-frequency variants. In this study, we used mixtures of mitochondrial samples with different haplogroups (i.e., a specific
Theresa Lüth +6 more
doaj +1 more source
Hypothesis testing procedure for binary and multi‐class F1‐scores in the paired design
Statistics in Medicine, 2023 In modern medicine, medical tests are used for various purposes including diagnosis, disease screening, prognosis, and risk prediction. To quantify the performance of the binary medical test, we often use sensitivity, specificity, and negative and ...
Kanae Takahashi +4 more
semanticscholar +1 more source
IEEE Access, 2021 Even if measuring the outcome of binary classifications is a pivotal task in machine learning and statistics, no consensus has been reached yet about which statistical rate to employ to this end.
D. Chicco, M. Warrens, Giuseppe Jurman
semanticscholar +1 more source
sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification
, 2021 Multiclass multilabel classification is the task of attributing multiple labels to examples via predictions. Current models formulate a reduction of the multilabel setting into either multiple binary classifications or multiclass classification, allowing for the use of existing loss functions (sigmoid, cross-entropy, logistic, etc.).
Bénédict, G. +3 more
openaire +3 more sources