Label Specific Features-Based Classifier Chains for Multi-Label Classification
Multi-label classification tackles the problems in which each instance is associated with multiple labels. Due to the interdependence among labels, exploiting label correlations is the main means to enhance the performances of classifiers and a variety of corresponding multi-label algorithms have be...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9035463/ |
| _version_ | 1819175762940395520 |
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| author | Wei Weng Da-Han Wang Chin-Ling Chen Juan Wen Shun-Xiang Wu |
| author_facet | Wei Weng Da-Han Wang Chin-Ling Chen Juan Wen Shun-Xiang Wu |
| author_sort | Wei Weng |
| collection | DOAJ |
| description | Multi-label classification tackles the problems in which each instance is associated with multiple labels. Due to the interdependence among labels, exploiting label correlations is the main means to enhance the performances of classifiers and a variety of corresponding multi-label algorithms have been proposed. Among those algorithms Classifier Chains (CC) is one of the most effective methods. It induces binary classifiers for each label, and these classifiers are linked in a chain. In the chain, the labels predicted by previous classifiers are used as additional features for the current classifier. The original CC has two shortcomings which potentially decrease classification performances: random label ordering, noise in original and additional features. To deal with these problems, we propose a novel and effective algorithm named LSF-CC, i.e. Label Specific Features based Classifier Chain for multi-label classification. At first, a feature estimating technique is employed to produce a list of most relevant features and labels for each label. According to these lists, we define a chain to guarantee that the most frequent labels that appear in these lists are top-ranked. Then, label specific features can be selected from the original feature space and label space. Based on these label specific features, corresponding binary classifiers are learned for each label. Experiments on several multi-label data sets from various domains have shown that the proposed method outperforms well-established approaches. |
| first_indexed | 2024-12-22T21:00:02Z |
| format | Article |
| id | doaj.art-9f95aeb7d8e0488b97e367a2870b8c02 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:00:02Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9f95aeb7d8e0488b97e367a2870b8c022022-12-21T18:12:51ZengIEEEIEEE Access2169-35362020-01-018512655127510.1109/ACCESS.2020.29805519035463Label Specific Features-Based Classifier Chains for Multi-Label ClassificationWei Weng0Da-Han Wang1https://orcid.org/0000-0002-5901-0778Chin-Ling Chen2https://orcid.org/0000-0002-4958-2043Juan Wen3Shun-Xiang Wu4College of Computer and Information Engineering, Xiamen University of Technology, Xiamen, ChinaCollege of Computer and Information Engineering, Xiamen University of Technology, Xiamen, ChinaCollege of Computer and Information Engineering, Xiamen University of Technology, Xiamen, ChinaDepartment of Statistics, School of Economics, Xiamen University, Xiamen, ChinaDepartment of Automation, Xiamen University, Xiamen, ChinaMulti-label classification tackles the problems in which each instance is associated with multiple labels. Due to the interdependence among labels, exploiting label correlations is the main means to enhance the performances of classifiers and a variety of corresponding multi-label algorithms have been proposed. Among those algorithms Classifier Chains (CC) is one of the most effective methods. It induces binary classifiers for each label, and these classifiers are linked in a chain. In the chain, the labels predicted by previous classifiers are used as additional features for the current classifier. The original CC has two shortcomings which potentially decrease classification performances: random label ordering, noise in original and additional features. To deal with these problems, we propose a novel and effective algorithm named LSF-CC, i.e. Label Specific Features based Classifier Chain for multi-label classification. At first, a feature estimating technique is employed to produce a list of most relevant features and labels for each label. According to these lists, we define a chain to guarantee that the most frequent labels that appear in these lists are top-ranked. Then, label specific features can be selected from the original feature space and label space. Based on these label specific features, corresponding binary classifiers are learned for each label. Experiments on several multi-label data sets from various domains have shown that the proposed method outperforms well-established approaches.https://ieeexplore.ieee.org/document/9035463/Classifier chainslabel specific featuresmulti-label learning |
| spellingShingle | Wei Weng Da-Han Wang Chin-Ling Chen Juan Wen Shun-Xiang Wu Label Specific Features-Based Classifier Chains for Multi-Label Classification IEEE Access Classifier chains label specific features multi-label learning |
| title | Label Specific Features-Based Classifier Chains for Multi-Label Classification |
| title_full | Label Specific Features-Based Classifier Chains for Multi-Label Classification |
| title_fullStr | Label Specific Features-Based Classifier Chains for Multi-Label Classification |
| title_full_unstemmed | Label Specific Features-Based Classifier Chains for Multi-Label Classification |
| title_short | Label Specific Features-Based Classifier Chains for Multi-Label Classification |
| title_sort | label specific features based classifier chains for multi label classification |
| topic | Classifier chains label specific features multi-label learning |
| url | https://ieeexplore.ieee.org/document/9035463/ |
| work_keys_str_mv | AT weiweng labelspecificfeaturesbasedclassifierchainsformultilabelclassification AT dahanwang labelspecificfeaturesbasedclassifierchainsformultilabelclassification AT chinlingchen labelspecificfeaturesbasedclassifierchainsformultilabelclassification AT juanwen labelspecificfeaturesbasedclassifierchainsformultilabelclassification AT shunxiangwu labelspecificfeaturesbasedclassifierchainsformultilabelclassification |