Name
Title | ||
|---|---|---|
A Graph-based Semi-supervised Multi-label Learning Method Based on Label Correlation Consistency |
Abstract | ||
|---|---|---|
Multi-label learning deals with the problem which each data example can be represented by an instance and associated with a set of labels, i.e., every example can be classified into multiple classes simultaneously. Most of the existing multi-label learning methods are supervised which cannot deal with such application scenarios where manually labeling the data is very expensive and time-consuming while the unlabeled data are very cheap and easy to obtain. This paper proposes an ensemble learning method which integrates multi-label learning and graph-based semi-supervised learning into one framework. The label correlation consistency is introduced to deal with the multi-label learning. The proposed method has been evaluated on five public multi-label datasets by comparing it with state-of-the-art supervised and semi-supervised multi-label methods according to multiple evaluation metrics to confirm its effectiveness. Experimental results show that the proposed method can achieve the comparable performance compared with the state-of-the-art methods. Furthermore, it is more confident on every single predicted label. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s12559-021-09912-y | COGNITIVE COMPUTATION |
Keywords | DocType | Volume |
Multi-label learning, Graph-based Semi-supervised Learning, Anchors, Label Correlation Consistency | Journal | 13 |
Issue | ISSN | Citations |
6 | 1866-9956 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qin Zhang | 1 | 0 | 0.34 |
| Guoqiang Zhong | 2 | 16 | 3.78 |
| Junyu Dong | 3 | 0 | 0.68 |