Multi-Label Takagi-Sugeno-Kang Fuzzy System

• URL: http://arxiv.org/abs/2309.11469v1
• Date: Wed, 20 Sep 2023 17:09:09 GMT
• Title: Multi-Label Takagi-Sugeno-Kang Fuzzy System
• Authors: Qiongdan Lou, Zhaohong Deng, Zhiyong Xiao, Kup-Sze Choi, Shitong Wang
• Abstract summary: We propose a new multi-label classification method, called Multi-Label Takagi-Sugeno-Kang Fuzzy System (ML-TSK FS) The structure of ML-TSK FS is designed using fuzzy rules to model the relationship between features and labels. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark multi-label datasets.
• Score: 22.759310690164227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label 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 the classification performance. To this end, we propose a new multi-label classification method, called Multi-Label 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 fuzzy system is trained by integrating fuzzy inference based multi-label correlation learning with multi-label regression loss. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark multi-label datasets. 1 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.

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