Multiple kernel learning for label relation and class imbalance in multi-label learning

There are two common challenges in multi-label learning (MLL), complex label relation and imbalanced class. Few studies have focused on addressing both problems at the same time. In this paper, we propose a multiple kernel learning (MKL) approach to tackle two challenges, named as Multi-Kernel Multi-Label (MKML) method. MKML contains three kernel modules. The first kernel module adopts the traditional kernel function which contains the global information, and the other two kernel modules are designed for the two problems respectively. The second kernel module learns the inter-label relation by adding supervised information. The third kernel module adjusts the imbalanced decision boundary through multi-layer fusion strategy, which is proved to improve the representation ability of kernels in this paper. Finally, the proposed joint optimization method in this MKL framework achieves good generalization ability. We conduct several related experiments using real-world datasets to evaluate the effectiveness of our method. The results demonstrate that MKML outperforms other state-of-the art methods in MLL task. (c) 2022 Elsevier Inc. All rights reserved.