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：模型透明性、标签关联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，现有方法鲜少能同时研究这三项特性。为应对这一挑战，我们提出了一种具有鲁棒性的多标签Takagi-Sugeno-Kang模糊系统（R-MLTSK-FS），该系统包含三种机制。首先，我们设计了一种软标签学习机制，通过显式衡量标签之间的相互作用来降低标签噪声的影响，该机制同时也是另外两种机制的基础。其次，我们采用基于规则的TSK模糊系统作为基础模型，以比许多现有模型更透明的方式高效地对特征与软标签之间的推理关系进行建模。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一个关联增强学习机制。大量实验证明了所提方法的优越性。