Set-valued Classification Via Evidential Deep Learning with Maximum Expected Utility

Conventional evidential deep learning (EDL) represents evidence strength and vacuity but typically converts the learned evidence into a single-label prediction. This may conceal class ambiguity when multiple classes remain plausible and lead to unreliable precise decisions. To address this problem, this paper proposes an evidential set-valued classification framework, termed EDL-SC. First, the decision space is extended from singleton labels to both singleton and class-pair actions, allowing class ambiguity to be explicitly represented. Second, a set-aware supervision term is incorporated into evidential learning to align the learned evidence with the expanded decision space. Third, a maximum expected utility rule is used to select between precise and set-valued predictions, with a risk preference parameter controlling the trade-off between true-class coverage and prediction imprecision. Extensive experiments demonstrate that EDL-SC consistently improves true-class coverage over precise EDL while maintaining selective set-valued outputs. Compared with randomized Adaptive Prediction Sets, it produces more compact prediction sets and achieves a more favorable utility-based balance between coverage and decision specificity. Further analyses show that MEU inference enables the transition from forced precise predictions to set-valued decisions, while set-aware learning helps refine when such decisions should be issued. Overall, EDL-SC provides a framework that aligns evidential learning with MEU-based set-valued decision making, thereby supporting the explicit representation of class ambiguity and controllable decision making under uncertainty.