Multi-label feature selection with high-level semantic label relationships based on fuzzy rough sets

In multi-label learning, feature selection plays a key role. This paper delves into the study of label relationships within the field of multi-label feature selection, with a particular focus on local label relationships. Most existing research explores these local relationships through clustering and other methods. These approaches may overlook the fact that the local correlations between labels may only manifest in specific subsets of the data, and there may be more complex semantic relationships between labels. Additionally, traditional logical label representation methods may not fully capture the connections between the sample space and the label space, whereas converting logical labels into label distributions with relative importance can provide more effective supervision information. At the same time, existing multi-label feature selection methods based on fuzzy rough sets are deficient in handling the interrelationships between labels and are sensitive to noise. Motivated by these challenges, this paper proposes a new multi-label feature selection algorithm that combines label enhancement and fuzzy rough sets. First, we propose a method to identify neighborhood granules with high-level semantic relationships to explore the local structure of the data, and based on this, we propose a novel label enhancement algorithm. Secondly, we transform the decision classes in traditional fuzzy rough set model into fuzzy decision classes, and propose an innovative multi-label fuzzy decision system based on label distribution. Finally, we design a new type of multi-label feature selection method based on high-level semantic relationships (HSR-MLFS). In the experiments, compared with other seven algorithms on eleven real-world datasets, the results show the superiority of the proposed algorithm.