Name
Abstract | ||
|---|---|---|
Multilabel (ML) classification can effectively identify the relevant labels of an instance from a given set of labels. However, the modeling of the relationship between the features and the labels is critical to classification performance. To this end, in this article, we propose a new ML classification method, called ML Takagi-Sugeno-Kang fuzzy system (ML-TSK FS), to improve the classification performance. The structure of ML-TSK FS is designed using fuzzy rules to model the relationship between features and labels. The FS is trained by integrating fuzzy inference-based ML correlation learning with ML regression loss. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark ML datasets. The results show that the performance of ML-TSK FS is competitive with existing methods in terms of various evaluation metrics, indicating that it is able to model the feature-label relationship effectively using fuzzy inference rules and enhances the classification performance. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TFUZZ.2021.3115967 | IEEE Transactions on Fuzzy Systems |
Keywords | DocType | Volume |
Fuzzy inference rules,label correlation learning,multilabel (ML) classification,multilabel Takagi-Sugeno-Kang (TSK) fuzzy system (FS) | Journal | 30 |
Issue | ISSN | Citations |
9 | 1063-6706 | 0 |
PageRank | References | Authors |
0.34 | 32 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiongdan Lou | 1 | 3 | 1.38 |
| Zhaohong Deng | 2 | 647 | 35.34 |
| Zhiyong Xiao | 3 | 0 | 0.34 |
| Kup-Sze Choi | 4 | 526 | 47.41 |
| Shitong Wang | 5 | 1485 | 109.13 |