  Addressing uncertainty in Deep Learning (DL) is essential, as it enables the
development of models that can make reliable predictions and informed decisions
in complex, real-world environments where data may be incomplete or ambiguous.
This paper introduces a novel algorithm leveraging Dempster-Shafer Theory (DST)
to integrate multiple pre-trained models to form an ensemble capable of
providing more reliable and enhanced classifications. The main steps of the
proposed method include feature extraction, mass function calculation, fusion,
and expected utility calculation. Several experiments have been conducted on
CIFAR-10 and CIFAR-100 datasets, demonstrating superior classification accuracy
of the proposed DST-based method, achieving improvements of 5.4% and 8.4%,
respectively, compared to the best individual pre-trained models. Results
highlight the potential of DST as a robust framework for managing uncertainties
related to data when applying DL in real-world scenarios.
