Expede Herculem: Learning Multi Labels From Single Label
Although there has been a lot of research in multi-label learning task, little attention has been paid on the weak label problem, in which only a subset of labels has been assigned to each instance in the training set. The extreme form of weak label learning is to predict all the labels from just on...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8491258/ |
| _version_ | 1831541911478861824 |
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| author | Dejun Mu Junhong Duan Xiaoyu Li Hang Dai Xiaoyan Cai Lantian Guo |
| author_facet | Dejun Mu Junhong Duan Xiaoyu Li Hang Dai Xiaoyan Cai Lantian Guo |
| author_sort | Dejun Mu |
| collection | DOAJ |
| description | Although there has been a lot of research in multi-label learning task, little attention has been paid on the weak label problem, in which only a subset of labels has been assigned to each instance in the training set. The extreme form of weak label learning is to predict all the labels from just one label set in the training phase. In this paper, we focus on dealing with this kind of weak label learning task, which is commonly met in old legacy information system, and it is also called “Hercules Learning.”We propose a label-group-optimization-based Hercules learning algorithm, which divides the entire label set into multiple groups according to the classifier's capability to distinguish them, so for each group, we can train a classifier which can predict instance's label within the group with high accuracy. The experimental results show that our algorithm is obviously superior to the existing weak label learning algorithm. |
| first_indexed | 2024-12-17T00:25:57Z |
| format | Article |
| id | doaj.art-496d0da3687749ffb710162733920ffe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:25:57Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-496d0da3687749ffb710162733920ffe2022-12-21T22:10:28ZengIEEEIEEE Access2169-35362018-01-016614106141810.1109/ACCESS.2018.28760148491258Expede Herculem: Learning Multi Labels From Single LabelDejun Mu0https://orcid.org/0000-0002-2568-0861Junhong Duan1Xiaoyu Li2Hang Dai3Xiaoyan Cai4https://orcid.org/0000-0002-1406-107XLantian Guo5https://orcid.org/0000-0002-1792-4926Shenzhen Research Institute, Northwestern Polytechnical University, Shenzhen, ChinaShenzhen Research Institute, Northwestern Polytechnical University, Shenzhen, ChinaShenzhen Research Institute, Northwestern Polytechnical University, Shenzhen, ChinaShenzhen Research Institute, Northwestern Polytechnical University, Shenzhen, ChinaShenzhen Research Institute, Northwestern Polytechnical University, Shenzhen, ChinaShenzhen Research Institute, Northwestern Polytechnical University, Shenzhen, ChinaAlthough there has been a lot of research in multi-label learning task, little attention has been paid on the weak label problem, in which only a subset of labels has been assigned to each instance in the training set. The extreme form of weak label learning is to predict all the labels from just one label set in the training phase. In this paper, we focus on dealing with this kind of weak label learning task, which is commonly met in old legacy information system, and it is also called “Hercules Learning.”We propose a label-group-optimization-based Hercules learning algorithm, which divides the entire label set into multiple groups according to the classifier's capability to distinguish them, so for each group, we can train a classifier which can predict instance's label within the group with high accuracy. The experimental results show that our algorithm is obviously superior to the existing weak label learning algorithm.https://ieeexplore.ieee.org/document/8491258/Multi-label classificationweak-supervised learninggenetic algorithm |
| spellingShingle | Dejun Mu Junhong Duan Xiaoyu Li Hang Dai Xiaoyan Cai Lantian Guo Expede Herculem: Learning Multi Labels From Single Label IEEE Access Multi-label classification weak-supervised learning genetic algorithm |
| title | Expede Herculem: Learning Multi Labels From Single Label |
| title_full | Expede Herculem: Learning Multi Labels From Single Label |
| title_fullStr | Expede Herculem: Learning Multi Labels From Single Label |
| title_full_unstemmed | Expede Herculem: Learning Multi Labels From Single Label |
| title_short | Expede Herculem: Learning Multi Labels From Single Label |
| title_sort | expede herculem learning multi labels from single label |
| topic | Multi-label classification weak-supervised learning genetic algorithm |
| url | https://ieeexplore.ieee.org/document/8491258/ |
| work_keys_str_mv | AT dejunmu expedeherculemlearningmultilabelsfromsinglelabel AT junhongduan expedeherculemlearningmultilabelsfromsinglelabel AT xiaoyuli expedeherculemlearningmultilabelsfromsinglelabel AT hangdai expedeherculemlearningmultilabelsfromsinglelabel AT xiaoyancai expedeherculemlearningmultilabelsfromsinglelabel AT lantianguo expedeherculemlearningmultilabelsfromsinglelabel |