Multi-label learning of missing labels using label-specific features: an embedded packaging method-Reference-Cited by-同舟云学术

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Multi-label learning of missing labels using label-specific features: an embedded packaging method

Published:2023-12-20 Issue:1 Volume:54 Page:791-814
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Zhao Dawei,Tan Yi,Sun Dong,Gao Qingwei,Lu Yixiang,Zhu De

Funder

Nature Science Foundation of Anhui

National Natural Science Foundation of China

Key Science Project of Anhui Education Department of China

CERNET Innovation Project of China

China Postdoctoral Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-05203-1.pdf

Reference56 articles.

1. Tsoumakas G,Katakis I, Vlahavas I (2009) Mining multi-label data. In: Data mining and knowledge discovery handbook, pp 667–685. Springer https://doi.org/10.1007/978-0-387-09823-4$_$34

2. Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surv (CSUR) 47(3):1–38. https://doi.org/10.1145/2716262

3. Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837. https://doi.org/10.1109/TKDE.2013.39

4. Ueda N, Saito K (2002) Parametric mixture models for multi-labeled. Adv Neural Inf Process Syst 15. https://proceedings.neurips.cc/paper/2002/file/3147da8ab4a0437c15ef51a5cc7f2dc4-Paper.pdf

5. Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771. https://doi.org/10.1016/j.patcog.2004.03.009

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