Toward embedding-based multi-label feature selection with label and feature collaboration-Reference-Cited by-同舟云学术

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Toward embedding-based multi-label feature selection with label and feature collaboration

Published:2022-10-27 Issue:6 Volume:35 Page:4643-4665
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Dai Liang,Zhang Jia,Du Guodong,Li Candong,Wei Rong,Li Shaozi^ORCID

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

the National Natural Science Foundation of Guangdong, China

the Fundamental Research Funds for the Central Universities

Startup Foundation for Introducing Talent of Yanshan University

Specific Research Project of Guangxi for Research Bases and Talents

Zhejiang Traditional Chinese Medicine Administration

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-07924-9.pdf

Reference54 articles.

1. Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837

2. Huang Z-A, Zhang J, Zhu Z, Wu EQ, Tan KC (2020) Identification of autistic risk candidate genes and toxic chemicals via multilabel learning. IEEE Trans Neur Netw Learn Sys 32(9):3971–3984

3. Zhang J, Li S, Jiang M, Tan KC (2020) Learning from weakly labeled data based on manifold regularized sparse model. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3015269

4. Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Patt Recognit 37(9):1757–1771

5. Dai L, Zhang J, Li C, Zhou C, Li S (2019) Multi-label feature selection with application to TCM state identification. Concurr Comput: Pract Exper 31(23):4634

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