Model transparency, label correlation learning and the robust-ness to label
noise are crucial for multilabel learning. However, few existing methods study
these three characteristics simultaneously. To address this challenge, we
propose the robust multilabel Takagi-Sugeno-Kang fuzzy system (R-MLTSK-FS) with
three mechanisms. First, we design a soft label learning mechanism to reduce
the effect of label noise by explicitly measuring the interactions between
labels, which is also the basis of the other two mechanisms. Second, the
rule-based TSK FS is used as the base model to efficiently model the inference
relationship be-tween features and soft labels in a more transparent way than
many existing multilabel models. Third, to further improve the performance of
multilabel learning, we build a correlation enhancement learning mechanism
based on the soft label space and the fuzzy feature space. Extensive
experiments are conducted to demonstrate the superiority of the proposed
method.Comment: This paper has been accepted by IEEE Transactions on Fuzzy System