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.

翻译：模型透明度、标签相关性学习以及对标签噪声的鲁棒性是多标签学习中的关键特性。然而，现有方法鲜有同时研究这三种特性。为应对这一挑战，我们提出了一种融合三种机制的鲁棒多标签Takagi-Sugeno-Kang模糊系统（R-MLTSK-FS）。首先，我们设计了一种软标签学习机制，通过显式度量标签间的相互作用来降低标签噪声的影响，该机制也是另外两种机制的基础。其次，以基于规则的TSK FS作为基模型，相比现有多种多标签模型能以更透明的方式高效建模特征与软标签之间的推理关系。最后，为进一步提升多标签学习性能，我们基于软标签空间与模糊特征空间构建了相关性增强学习机制。大量实验证明了所提方法的优越性。