期刊
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
卷 32, 期 22, 页码 -出版社
WILEY
DOI: 10.1002/cpe.5864
关键词
feature selection; label correlations; machine learning; multilabel classification
资金
- Collaborative Innovation Center of Chinese Oo-long Tea Industry-Collaborative Innovation Center (2011) of Fujian Province
- Fujian Province 2011 Collaborative Innovation Center of TCM Health Management
- National Key Research and Development Program of China [2018YFC0831402]
- National Nature Science Foundation of China [61806172, 61876159, U1705286]
In recent years, multilabel learning has been applied to a lot of application areas and is yet a challenging task. In multilabel learning, an instance often belongs to multiple class labels simultaneously. The labels usually have correlations with others, and mining label correlations is helpful to enhance the multilabel classification performance. Aiming at increasing the accuracy of prediction, Label embedding (LE) is an important technique, and conducive to extracting label information for multilabel learning. In this paper, we present a novel multilabel learning approach via exploiting label correlations, which can be naturally extended to tackle feature selection problem. First, to obtain the discriminative features shared by all labels, the proposed algorithm learns a latent space by employing deep canonical correlation analysis. Then we exploit label correlations by enforcing predictions on similar labels to be similar, thereby improving the prediction performance. Results on several multiple datasets illustrate that the proposed algorithm has the advantages on multilabel classification and feature selection.
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