Dual-channel graph contrastive learning for multi-label classification with label-specific features and label correlations

Xiaoyan Zhu; Tong Zhu; Jiaxuan Li; Jiayin Wang

doi:10.1007/s00521-024-09810-y

ScienceGate Book Chapters

JOURNAL ARTICLE

Dual-channel graph contrastive learning for multi-label classification with label-specific features and label correlations

Xiaoyan Zhu Tong Zhu Jiaxuan Li Jiayin Wang

Year: 2024 Journal: Neural Computing and Applications Vol: 36 (23)Pages: 14483-14502 Publisher: Springer Science+Business Media

DOI: 10.1007/s00521-024-09810-y

Get Full-Text PDF Get Analytical Report

Keywords:

Multi-label classification Computer science Artificial intelligence Pattern recognition (psychology) Graph Dual (grammatical number) Natural language processing Theoretical computer science Linguistics

Metrics

Cited By

0.64

FWCI (Field Weighted Citation Impact)

Refs

0.64

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

Topic Modeling

Physical Sciences → Computer Science → Artificial Intelligence

Spam and Phishing Detection

Physical Sciences → Computer Science → Information Systems

Dual-channel graph contrastive learning for multi-label classification with label-specific features and label correlations

Metrics

Citation History

Topics

Related Documents

Multi-label learning with label-specific features by resolving label correlations

Learning Label Specific Features for Multi-label Classification

Gandalf: Learning Label-label Correlations in Extreme Multi-label Classification via Label Features

Learning multi-label label-specific features via global and local label correlations

Exploring Common and Label-Specific Features for Multi-Label Learning With Local Label Correlations