Beyond Equal Views: Strength-Adaptive Evidential Multi-View Learning

Multi-view learning plays a pivotal role in enabling intelligent systems to understand the world from diverse perspectives. While recent advances in evidential multi-view learning have introduced uncertainty-aware mechanisms, most existing methods treat all views equally during fusion-overlooking the disparity in evidence strength across views. In real-world scenarios, however, some views may offer weaker yet clearer category cues, which deserve greater emphasis during integration. In this paper, we propose a Strength-Adaptive Evidential Multi-View Learning (SAEML) method that performs reliability-aware fusion by explicitly modeling the contribution of each view's evidence. Our method introduces Fisher-evidential networks to preserve potentially valuable category-wise evidence for downstream fusion through multi-peaked outputs, and assesses view contributions from three key aspects: (1) self-view belief mass reflecting internal evidence distribution, (2) cross-view belief mass capturing complementarity between views, and (3) category-aware uncertainty mass modeling reliability at a fine-grained level. These metrics jointly guide a one-step, weighted fusion strategy that avoids information dilution and enhances multi-view complementarity. Experiments on six datasets validate the effectiveness of SAEML, including a 7% performance gain over SOTA methods on the 3-sources dataset. The code is released at https://github.com/Wednesque/SAEML.