Multi-label learning with label-specific features via weighting and label entropy guided clustering ensemble. Zhang, C. & Li, Z. Neurocomputing, 419:59–69, January, 2021. ![Multi-label learning with label-specific features via weighting and label entropy guided clustering ensemble [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Multi-label learning has attracted more and more researchers’ attention. It deals with the problem where each instance is associated with multiple labels simultaneously. Some methods improve the performance by constructing label-specific features. Specifically, the LIFTACE method constructs label-specific features by clustering ensemble techniques, which ignores the importance of label vectors and does not explore label correlations when constructing the classification model. In this paper, we propose a multi-label learning method called LF-LELC, which considers the importance of label vectors and constructs the classification model by considering label correlations. Firstly, it performs clustering on the positive instances and negative instances respectively. The number of clusters is set by the information contained in the label vectors. After that, it employs clustering ensemble techniques that consider label correlations to make the clustering results more stable and effective. Then, it constructs label-specific features for each label. Finally, it builds the classification model by exploring label correlations. The label set for each test example is predicted by the classification model. Experiments show that LF-LELC can achieve better performance by considering the importance of label vectors and the correlations among labels.
@article{zhang_multi-label_2021,
title = {Multi-label learning with label-specific features via weighting and label entropy guided clustering ensemble},
volume = {419},
issn = {0925-2312},
url = {https://www.sciencedirect.com/science/article/pii/S0925231220313059},
doi = {10.1016/j.neucom.2020.07.107},
abstract = {Multi-label learning has attracted more and more researchers’ attention. It deals with the problem where each instance is associated with multiple labels simultaneously. Some methods improve the performance by constructing label-specific features. Specifically, the LIFTACE method constructs label-specific features by clustering ensemble techniques, which ignores the importance of label vectors and does not explore label correlations when constructing the classification model. In this paper, we propose a multi-label learning method called LF-LELC, which considers the importance of label vectors and constructs the classification model by considering label correlations. Firstly, it performs clustering on the positive instances and negative instances respectively. The number of clusters is set by the information contained in the label vectors. After that, it employs clustering ensemble techniques that consider label correlations to make the clustering results more stable and effective. Then, it constructs label-specific features for each label. Finally, it builds the classification model by exploring label correlations. The label set for each test example is predicted by the classification model. Experiments show that LF-LELC can achieve better performance by considering the importance of label vectors and the correlations among labels.},
language = {en},
urldate = {2021-10-18},
journal = {Neurocomputing},
author = {Zhang, Chunyu and Li, Zhanshan},
month = jan,
year = {2021},
keywords = {Label correlation, Label entropy, Label-specific features, Multi-label learning},
pages = {59--69},
}
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In this paper, we propose a multi-label learning method called LF-LELC, which considers the importance of label vectors and constructs the classification model by considering label correlations. Firstly, it performs clustering on the positive instances and negative instances respectively. The number of clusters is set by the information contained in the label vectors. After that, it employs clustering ensemble techniques that consider label correlations to make the clustering results more stable and effective. Then, it constructs label-specific features for each label. Finally, it builds the classification model by exploring label correlations. The label set for each test example is predicted by the classification model. 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