Parallel-serial architecture with instance correlation label-specific features for multi-label learning

Feature extraction plays a crucial role in capturing data correlations, thereby improving the performance of multi-label learning models. Popular approaches mainly include feature space manipulation techniques, such as recursive feature elimination, and feature alternative techniques, such as label-specific feature extraction. However, the former does not utilize label information, while the latter does not consider correlation among instances. In this study, we propose a label-specific feature extraction approach embedding instance correlation by a joint loss function under a parallel-serial architecture (LSIC-PS). Our approach incorporates three main techniques. First, we employ a parallel isomorphic network to extract label-specific features, which are directly integrated into a serial network to enhance label correlation. Second, we introduce instance correlation to guide feature extraction in parallel networks, leveraging label information from other instances to improve generalization. Third, we design a parameter-setting strategy to control a new joint loss function, adapting its instance correlation proportion to different datasets. We conduct experiments on sixteen widely used datasets and compare the results of our approach with those of twelve popular algorithms. Across eight evaluation metrics, LSIC-PS demonstrates state-of-art performance in multi-label learning. The source code is available at github.com/fansmale/lsic-ps.