Evidential Reliable Fusion for Partial Multi-view Incomplete Multi-label Classification

In recent years, the missing data problem in multi view multi-label classification (MvMlC) has attracted extensive attention from researchers, with numerous solutions for partial multi-view incomplete multi-label classification (PMvIMlC) emerging. Nevertheless, two critical challenges persist. One is suboptimal coarse-grained multi-view fusion: traditional dynamic fusion at the view level is unable to accommodate the practical fusion demands of samples with diverse qualities. The other is neglecting latent information within missing labels: during the training phase, existing works only focus on the limited supervised information of unmissing labels while ignoring the underlying information at missing positions. To address these issues, we propose Evidential Reliable Fusion for Partial Multi view Incomplete Multi-label Classification, termed ERF. ERF comprises two core modules: 1) Uncertainty-guided fusion module via evidence theory and 2) adaptive negative label pseudo labeling. The former quantifies sample-level uncertainty of each view based on evidence theory, which is then used to guide multi-view fusion, enabling a fine-grained, instance-level multi view fusion scheme. For the latter, leveraging the model's perception ability for neighboring samples in the label space, we design a strategy to select reliable negative pseudo-labels. This module enhances supervisory information to aid model training by recovering reliable negative pseudo-labels. Extensive experiments demonstrate that our ERF delivers significantly superior classification performance over existing methods.