Multi-level correlation information fusion via three-way concept-cognitive learning for multi-label learning

Abstract

Multi-label learning tasks typically involve complex correlation between labels, which often span across multiple levels. Accurately capturing and fusing these multi-level correlation information is crucial for improving prediction performance and understanding the potential relationship between labels. The current mainstream label correlation acquisition methods mainly focus on statistical analysis of labels. However, these methods lack exploration of the hierarchical structure of correlation, which may lead to the cognitive bias of labels and the decline in predictive performance. To address this, a multi-label learning model with multi-level correlation information fusion via three-way concept-cognitive learning (MCF-3WCCL) is proposed to capture the hierarchical correlation between labels more comprehensively, improve the prediction performance and enhance the interpretability. In this model, three-way concept-cognitive operators are utilized to structurally represent label concepts, thereby capturing the hierarchical correlations among labels. Additionally, the extent of label concepts are used as clues, which are mapped into feature concepts to form the dependencies between labels and features. On this basis, by fusing these feature concepts, the overall cognition of the label is finally formed. Extensive comparative experiments reflect that the proposed method is superior and versatile.