Results 21 to 30 of about 101,283 (263)
Learning Common and Label-Specific Features for Multi-Label Classification With Missing Labels
IEEE AccessMulti-label learning is a subfield of machine learning that addresses the issue of each instance belonging to numerous class labels at the same time. However, in some real applications, we can only receive a partial set of labels for each instance due to
Runxin Li +4 more
doaj +1 more source
Multi-Label Bioinformatics Data Classification With Ensemble Embedded Feature Selection
IEEE Access, 2019 In bioinformatics, the vast of multi-label type of datasets, including clinical text, gene, and protein data, need to be categorized. Specifically, due to the redundant or irrelevant features in bioinformatics data, the performance of multi-label ...
Yumeng Guo +3 more
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT As part of the European Cooperative Study Group for Paediatric Rare Tumours initiative, we developed standard clinical practice guidelines for ovarian sex cord stromal tumors, based on comprehensive national and international cohort analyses, literature review, and a final expert consensus conference.
Dominik T. Schneider +15 more
wiley +1 more source
Metric Learning-Based Multi-Instance Multi-Label Classification With Label Correlation
IEEE Access, 2019 In multi-instance multi-label learning (MIML) problems, predicting the labels of unseen bags becomes difficult when the labels of their instances are not provided directly.
Haifeng Hu +3 more
doaj +1 more source
Correlative multi-label video annotation [PDF]
Proceedings of the 15th ACM international conference on Multimedia, 2007 Automatically annotating concepts for video is a key to semantic-level video browsing, search and navigation. The research on this topic evolved through two paradigms. The first paradigm used binary classification to detect each individual concept in a concept set.
Guo-Jun Qi +5 more
openaire +1 more source
A Bibliometric Analysis of Publications in Uremic Toxins From 1991 to 2024
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Uremic toxins are a growing area of research in nephrology, with significant implications in the progression and treatment of chronic kidney disease (CKD) and the management of end‐stage kidney disease (ESKD). This bibliometric analysis aims to evaluate the global research trends, key contributors, and the impact of publications in ...
Yuh‐Shan Ho +7 more
wiley +1 more source
Application of Label Correlation in Multi-Label Classification: A Survey
Applied SciencesMulti-Label Classification refers to the classification task where a data sample is associated with multiple labels simultaneously, which is widely used in text classification, image classification, and other fields. Different from the traditional single-
Shan Huang +6 more
doaj +1 more source
Multilabel Learning with Incomplete Using Dual-Manifold Mapping [PDF]
Jisuanji gongchengIn multilabel learning, the classification performance can be improved through the effective use of label correlations. However, owing to the subjectivity of manual tagging and the similarity of label semantics in practical applications, an incomplete ...
XU Zhilei, HUANG Rui
doaj +1 more source
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Chronic micro‐inflammation in patients with end‐stage renal disease (ESRD) is a significant driver of cardiovascular complications and diminished quality of life. While standard hemodialysis (SHD) effectively manages small‐molecule clearance, its ability to remove medium‐to‐large uremic toxins—the primary catalysts of systemic ...
Hongwei Zuo +5 more
wiley +1 more source
OMAL: A Multi-Label Active Learning Approach from Data Streams
EntropyWith the rapid growth of digital computing, communication, and storage devices applied in various real-world scenarios, more and more data have been collected and stored to drive the development of machine learning techniques.
Qiao Fang +7 more
doaj +1 more source