Hypergraph-guided Global Mean-field Negotiation For Multiview Evidential Classification

Multiview learning integrates complementary information from diverse observations to enhance model performance, where to quantify predictive uncertainty evidential deep learning has recently gained significant attention. However, existing methods generally overlook the critical importance of high-order correlation structures in multiview features, and traditional evidential combination rules frequently suffer from issues such as order dependence and irrational belief assignments when handling highly conflictive multiview features. To address these issues, we propose the Hypergraph-guided Global Mean-field Negotiation (HGMN) method for Multiview Evidential Classification. Specifically, HGMN first leverages hypergraph convolutional networks to capture high-order topological correlations within each view. Subsequently, instead of relying on pairwise or hierarchical fusion strategies, HGMN introduces a global mean-field negotiation mechanism that enables multiple views to dynamically reach a global consensus, effectively isolating dissenting noise. Finally, HGMN incorporates a multi-objective collaborative optimization strategy that enhances decision robustness and trustworthiness in complex open-world environments. Extensive experimental results on eight public datasets demonstrate that our method significantly outperforms state-of-the-art baselines in terms of accuracy and robustness.