Interactive Causal Correlation Space Reshape for Multi-Label Classification
Most existing multi-label classification models focus on distance metrics and feature spare strategies to extract specific features of labels. Those models use the cosine similarity to construct the label correlation matrix to constraint solution space, and then mine the latent semantic information...
Main Authors: | Chao Zhang, Yusheng Cheng, Yibin Wang, Yuting Xu |
---|---|
Format: | Article |
Sprog: | English |
Udgivet: |
Universidad Internacional de La Rioja (UNIR)
2022-09-01
|
Serier: | International Journal of Interactive Multimedia and Artificial Intelligence |
Fag: | |
Online adgang: | https://www.ijimai.org/journal/bibcite/reference/3159 |
Lignende værker
-
Application of Label Correlation in Multi-Label Classification: A Survey
af: Shan Huang, et al.
Udgivet: (2024-10-01) -
Robust Multi-Label Classification with Enhanced Global and Local Label Correlation
af: Tianna Zhao, et al.
Udgivet: (2022-05-01) -
Soft-label recover based label-specific features learning
af: Jiansheng Jiang, et al.
Udgivet: (2024-10-01) -
ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method
af: Xiangeng Wang, et al.
Udgivet: (2019-09-01) -
Joint Label-Density-Margin Space and Extreme Elastic Net for Label-Specific Features
af: Gensheng Pei, et al.
Udgivet: (2019-01-01)