期刊
KNOWLEDGE-BASED SYSTEMS
卷 159, 期 -, 页码 148-157出版社
ELSEVIER
DOI: 10.1016/j.knosys.2018.07.003
关键词
Multi-label learning; Optimization framework; Label-specific features; Label correlations; Traditional Chinese medicine
资金
- National Natural Science Foundation of China [61572409, U1705286, 61571188, 61672272]
- Fujian Province 2011 Collaborative Innovation Center of TCM Health Management
- Collaborative Innovation Center of Chinese Oolong Tea Industry-Collaborative Innovation Center (2011) of Fujian Province, Fund for Integration of Cloud Computing and Big Data, Innovation of Science and Education
In multi-label learning, different labels may have their own inherent characteristics for distinguishing each other, in the meanwhile, exploiting the correlations among labels is another practical yet challenging task to improve the performance. In this work, we present a new method for the joint learning of label-specific features and label correlations. The key is the design of an optimization framework to learn the weight assignment scheme of features, and the correlations among labels are taken into account by constructing additional features at the same time. Through iteratively optimizing the two sets of unknown variables, which are referred to feature weights and label correlations-based features, label-specific features of each label are available to achieve multi label classification. Comprehensive experiments on various multi-label data sets including two collected traditional Chinese medicine data sets reveal the advantages of our proposed algorithm.
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