A multi-label classification method for disposing incomplete labeled data and label relevance
Multi-label classification methods have been applied in many real-world fields，in which the labels may have strong relevance and some of them even are incomplete or missing.However，existing multi-label classification algorithms are unable to handle both issues simultaneously.A new probabilistic mode...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2016-08-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016197/ |
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| Summary: | Multi-label classification methods have been applied in many real-world fields，in which the labels may have strong relevance and some of them even are incomplete or missing.However，existing multi-label classification algorithms are unable to handle both issues simultaneously.A new probabilistic model that can automatically learn and exploit multi-label relevance was proposed on label relevance and missing label classification simultaneously.By integrating out the missing information，it also provides a disciplined approach to handle missing labels.Experiments on a number of real world data sets with both complete and incomplete labels demonstrated that the proposed method can achieve higher classification and prediction evaluation scores than the existing multi-label classification algorithms. |
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| ISSN: | 1000-0801 |