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Model transparency, label correlation learning and the robust-ness to labelnoise are crucial for multilabel learning. However, few existing methods studythese three characteristics simultaneously. To address this challenge, wepropose the robust multilabel Takagi-Sugeno-Kang fuzzy system (R-MLTSK-FS) withthree mechanisms. First, we design a soft label learning mechanism to reducethe effect of label noise by explicitly measuring the interactions betweenlabels, which is also the basis of the other two mechanisms. Second, therule-based TSK FS is used as the base model to efficiently model the inferencerelationship be-tween features and soft labels in a more transparent way thanmany existing multilabel models. Third, to further improve the performance ofmultilabel learning, we build a correlation enhancement learning mechanismbased on the soft label space and the fuzzy feature space. Extensiveexperiments are conducted to demonstrate the superiority of the proposedmethod.

A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance