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.Comment: This work has been accepted by IEEE Transactions on Fuzzy System