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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| Format: | Article |
| Language: | zho |
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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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| _version_ | 1841529931269406720 |
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| author | Lina ZHANG Lingpeng DAI Tai KUANG |
| author_facet | Lina ZHANG Lingpeng DAI Tai KUANG |
| author_sort | Lina ZHANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f7c5ff40862740a09ec44287a6abd238 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2016-08-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-f7c5ff40862740a09ec44287a6abd2382025-01-15T03:14:30ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012016-08-0132828959607740A multi-label classification method for disposing incomplete labeled data and label relevanceLina ZHANGLingpeng DAITai KUANGMulti-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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016197/incomplete labellabel relevancemulti-label classificationprobabilistic model |
| spellingShingle | Lina ZHANG Lingpeng DAI Tai KUANG A multi-label classification method for disposing incomplete labeled data and label relevance Dianxin kexue incomplete label label relevance multi-label classification probabilistic model |
| title | A multi-label classification method for disposing incomplete labeled data and label relevance |
| title_full | A multi-label classification method for disposing incomplete labeled data and label relevance |
| title_fullStr | A multi-label classification method for disposing incomplete labeled data and label relevance |
| title_full_unstemmed | A multi-label classification method for disposing incomplete labeled data and label relevance |
| title_short | A multi-label classification method for disposing incomplete labeled data and label relevance |
| title_sort | multi label classification method for disposing incomplete labeled data and label relevance |
| topic | incomplete label label relevance multi-label classification probabilistic model |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016197/ |
| work_keys_str_mv | AT linazhang amultilabelclassificationmethodfordisposingincompletelabeleddataandlabelrelevance AT lingpengdai amultilabelclassificationmethodfordisposingincompletelabeleddataandlabelrelevance AT taikuang amultilabelclassificationmethodfordisposingincompletelabeleddataandlabelrelevance AT linazhang multilabelclassificationmethodfordisposingincompletelabeleddataandlabelrelevance AT lingpengdai multilabelclassificationmethodfordisposingincompletelabeleddataandlabelrelevance AT taikuang multilabelclassificationmethodfordisposingincompletelabeleddataandlabelrelevance |