The article presents a new method for multi-label classification, the Multi-Label Takagi-Sugeno-Kang Fuzzy System (ML-TSK FS). This system uses fuzzy rules to model the relationship between features and labels, improving classification performance. The system is trained by integrating fuzzy inference-based multi-label correlation learning with multi-label regression loss. The performance of ML-TSK FS is evaluated on 12 benchmark multi-label datasets, showing competitive results with other methods. The paper suggests that the ML-TSK FS effectively models the feature-label relationship using fuzzy inference rules and enhances the classification performance.
Publication date: not provided
Project Page: not provided
Paper: https://arxiv.org/pdf/2309.11469
