Wanfu Gao

Pattern Recognit., 2025

Integrating label confidence-based feature selection for partial multi-label learning.

[BibT_eX]

[DOI]

Qingqi Han

Pattern Recognit., 2025

Fusion-enhanced multi-label feature selection with sparse supplementation.

[BibT_eX]

[DOI]

Inf. Fusion, 2025

Enhanced multi-label feature selection considering label-specific relevant information.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2025

2024

Multi-label feature selection with high-sparse personalized and low-redundancy shared common features.

[BibT_eX]

[DOI]

Inf. Process. Manag., March, 2024

Feature relevance and redundancy coefficients for multi-view multi-label feature selection.

[BibT_eX]

[DOI]

Qingqi Han

Inf. Sci., January, 2024

Label generation with consistency on the graph for multi-label feature selection.

[BibT_eX]

[DOI]

Inf. Sci., 2024

Anchor-guided global view reconstruction for multi-view multi-label feature selection.

[BibT_eX]

[DOI]

Kunpeng Liu

Inf. Sci., 2024

Exploring view-specific label relationships for multi-view multi-label feature selection.

[BibT_eX]

[DOI]

Inf. Sci., 2024

Escaping the neutralization effect of modality features fusion in multimodal Fake News Detection.

[BibT_eX]

[DOI]

Inf. Fusion, 2024

TFWT: Tabular Feature Weighting with Transformer.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Double-Layer Hybrid-Label Identification Feature Selection for Multi-View Multi-Label Learning.

[BibT_eX]

[DOI]

Kunpeng Liu

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Partial multi-label feature selection via subspace optimization.

[BibT_eX]

[DOI]

Inf. Sci., November, 2023

AdaNS: Adaptive negative sampling for unsupervised graph representation learning.

[BibT_eX]

[DOI]

Pattern Recognit., April, 2023

Multilabel Feature Selection With Constrained Latent Structure Shared Term.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., March, 2023

Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation.

[BibT_eX]

[DOI]

Pattern Recognit., 2023

Multi-label feature selection via robust flexible sparse regularization.

[BibT_eX]

[DOI]

Pattern Recognit., 2023

A unified low-order information-theoretic feature selection framework for multi-label learning.

[BibT_eX]

[DOI]

Pattern Recognit., 2023

Multi-Label Feature Selection Based on Min-Relevance Label.

[BibT_eX]

[DOI]

Hanlin Pan

IEEE Access, 2023

2022

Graph Multihead Attention Pooling with Self-Supervised Learning.

[BibT_eX]

[DOI]

Entropy, December, 2022

Multi-label feature selection method based on dynamic weight.

[BibT_eX]

[DOI]

Soft Comput., 2022

Robust multi-label feature selection with shared label enhancement.

[BibT_eX]

[DOI]

Juncheng Hu

Knowl. Inf. Syst., 2022

Label correlations variation for robust multi-label feature selection.

[BibT_eX]

[DOI]

Inf. Sci., 2022

Feature-specific mutual information variation for multi-label feature selection.

[BibT_eX]

[DOI]

Inf. Sci., 2022

Dynamic subspace dual-graph regularized multi-label feature selection.

[BibT_eX]

[DOI]

Neurocomputing, 2022

2021

Preserving Similarity and Staring Decisis for Feature Selection.

[BibT_eX]

[DOI]

IEEE Trans. Artif. Intell., 2021

Multi-label feature selection considering label supplementation.

[BibT_eX]

[DOI]

Pattern Recognit., 2021

Learning flexible network representation via anonymous walks.

[BibT_eX]

[DOI]

Yu Wang

Knowl. Based Syst., 2021

Multi-label feature selection based on the division of label topics.

[BibT_eX]

[DOI]

Inf. Sci., 2021

A conditional-weight joint relevance metric for feature relevancy term.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2021

Feature relevance term variation for multi-label feature selection.

[BibT_eX]

[DOI]

Appl. Intell., 2021

2020

Multi-label feature selection with shared common mode.

[BibT_eX]

[DOI]

Pattern Recognit., 2020

Robust multi-label feature selection with dual-graph regularization.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2020

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption.

[BibT_eX]

[DOI]

Entropy, 2020

Feature selection considering Uncertainty Change Ratio of the class label.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2020

Feature redundancy term variation for mutual information-based feature selection.

[BibT_eX]

[DOI]

Appl. Intell., 2020

Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection.

[BibT_eX]

[DOI]

IEEE Access, 2020

2019

Distinguishing two types of labels for multi-label feature selection.

[BibT_eX]

[DOI]

Guixia Liu

Pattern Recognit., 2019

2018

Feature selection considering the composition of feature relevancy.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2018

Class-specific mutual information variation for feature selection.

[BibT_eX]

[DOI]

Pattern Recognit., 2018

Feature selection considering two types of feature relevancy and feature interdependency.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2018

Feature selection by integrating two groups of feature evaluation criteria.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2018

Feature selection considering weighted relevancy.

[BibT_eX]

[DOI]

Guixia Liu

Appl. Intell., 2018

Feature Selection by Maximizing Part Mutual Information.

[BibT_eX]

[DOI]