  We present a sample of 485 photometrically identified Type Ia supernova
candidates mined from the first three years of data of the CFHT SuperNova
Legacy Survey (SNLS). The images were submitted to a deferred processing
independent of the SNLS real-time detection pipeline. Light curves of all
transient events were reconstructed in the g_M, r_M, i_M and z_M filters and
submitted to automated sequential cuts in order to identify possible
supernovae. Pure noise and long-term variable events were rejected by light
curve shape criteria. Type Ia supernova identification relied on event
characteristics fitted to their light curves assuming the events to be normal
SNe Ia. The light curve fitter SALT2 was used for this purpose, assigning host
galaxy photometric redshifts to the tested events. The selected sample of 485
candidates is one magnitude deeper than that allowed by the SNLS spectroscopic
identification. The contamination by supernovae of other types is estimated to
be 4%. Testing Hubble diagram residuals with this enlarged sample allows us to
measure the Malmquist bias due to spectroscopic selections directly. The result
is fully consistent with the precise Monte Carlo based estimate used to correct
SN Ia distance moduli in the SNLS 3-year cosmological analyses. This paper
demonstrates the feasibility of a photometric selection of high redshift
supernovae with known host galaxy redshifts, opening interesting prospects for
cosmological analyses from future large photometric SN Ia surveys.
