Multi-label feature selection by strongly relevant label gain and label mutual aid-Reference-Cited by-同舟云学术

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Multi-label feature selection by strongly relevant label gain and label mutual aid

Published:2024-01 Issue: Volume:145 Page:109945
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Dai Jianhua^ORCID,Huang Weiyi,Zhang Chucai,Liu Jie

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

National Office for Philosophy and Social Sciences

Science and Technology Program of Hunan Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference40 articles.

1. Attribute selection for partially labeled categorical data by rough set approach;Dai;IEEE Trans. Cybern.,2017

2. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy;Peng;IEEE Trans. Pattern Anal. Mach. Intell.,2005

3. A multi-label classification with an adversarial-based denoising autoencoder for medical image annotation;Chai;ACM Trans. Manage. Inf. Syst.,2023

4. Multi-label video classification via coupling attentional multiple instance learning with label relation graph;Li;Pattern Recognit. Lett.,2022

5. Hybrid embedding-based text representation for hierarchical multi-label text classification;Ma;Expert Syst. Appl.,2022

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2. Discriminative label correlation based robust structure learning for multi-label feature selection;Pattern Recognition;2024-10

3. Anchor-guided global view reconstruction for multi-view multi-label feature selection;Information Sciences;2024-09

4. Multilabel Feature Selection Based on Fuzzy Mutual Information and Orthogonal Regression;IEEE Transactions on Fuzzy Systems;2024-09

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