Towards a unified multi-source-based optimization framework for multi-label learning

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Jia Zhang, Candong Li, Zhenqiang Sun, Zhiming Luo, Changen Zhou, Shaozi Li

Published: 2019, Last Modified: 06 Nov 2025Appl. Soft Comput. 2019EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•We propose a multi-source-based optimization framework for multi-label learning.•We consider multi-label consensus learning by preserving the label correlations.•The proposed method can combine with other multi-label methods freely.•The proposed method is effective for long-tail data.•Experiments on various data sets reveal the advantages of the proposed method.