Dual Set Multi-Label Learning

• DOI: 10.1609/aaai.v32i1.11695
• Publication Date: 2022-06-24T21:08:34Z
• Authors: Chong Liu; Peng Zhao; Sheng-Jun Huang; Yuan Jiang; Zhi-Hua Zhou
• Keywords: Boosting; Multi-label classification

In this paper, we propose a new learning framework named dual set multi-label learning, where there are two sets of labels, and an object has one only positive label in each set. Compared to general the exclusive relationship among labels within same set, pairwise inter-set much more explicit likely be fully exploited. To handle such kind problems, novel boosting style algorithm with model-reuse distribution adjusting mechanisms is proposed make help other. addition, theoretical analyses presented show superiority from directly all labels. empirically evaluate performance our approach, conduct experiments on manually collected real-world datasets along adapted dataset. Experimental results validate effectiveness approach for learning.