  As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the
Dark Energy Survey (DES), we present photometrically identified SN Ia samples
using multi-band light-curves and host galaxy redshifts. For this analysis, we
use the photometric classification framework SuperNNova (SNN; M\"oller et al.
2019) trained on realistic DES-like simulations. For reliable classification,
we process the DES SN programme (DES-SN) data and introduce improvements to the
classifier architecture, obtaining classification accuracies of more than 98
per cent on simulations. This is the first SN classification to make use of
ensemble methods, resulting in more robust samples. Using photometry, host
galaxy redshifts, and a classification probability requirement, we identify
1,863 SNe Ia from which we select 1,484 cosmology-grade SNe Ia spanning the
redshift range of 0.07 < z < 1.14. We find good agreement between the
light-curve properties of the photometrically-selected sample and simulations.
Additionally, we create similar SN Ia samples using two types of Bayesian
Neural Network classifiers that provide uncertainties on the classification
probabilities. We test the feasibility of using these uncertainties as
indicators for out-of-distribution candidates and model confidence. Finally, we
discuss the implications of photometric samples and classification methods for
future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and
Time (LSST).
