Adaptive label secondary reconstruction for missing multi-label learning-Reference-Cited by-同舟云学术

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Adaptive label secondary reconstruction for missing multi-label learning

Published:2024-09 Issue: Volume:299 Page:112019
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Qin Zhi,Chen Hongmei^ORCID,Yin Tengyu,Yuan Zhong,Luo Chuan,Horng Shi-Jinn^ORCID,Li Tianrui

Publisher

Elsevier BV

Reference50 articles.

1. Feature selection for handling label ambiguity using weighted label-fuzzy relevancy and redundancy;Deng;IEEE Trans. Fuzzy Syst.,2024

2. Multi-label text classification based on semantic-sensitive graph convolutional network;Zeng;Knowl.-Based Syst.,2024

3. Deep co-image-label hashing for multi-label image retrieval;Shen;IEEE Trans. Multimed.,2022

4. PFmulDL: A novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods;Xia;Comput. Biol. Med.,2022

5. Novel multi-label feature selection via label symmetric uncertainty correlation learning and feature redundancy evaluation;Dai;Knowl.-Based Syst.,2020

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