Shi-Qi Yang

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...