  Multivariate methods have been recently introduced and successfully applied
for the discrimination of signal from background in the selection of genuine
very-high energy gamma-ray events with the H.E.S.S. Imaging Atmospheric
Cerenkov Telescope. The complementary performance of three independent
reconstruction methods developed for the H.E.S.S. data analysis, namely Hillas,
model and 3D-model suggests the optimization of their combination through the
application of a resulting efficient multivariate estimator. In this work the
boosted decision tree method is proposed leading to a significant increase in
the signal over background ratio compared to the standard approaches. The
improved sensitivity is also demonstrated through a comparative analysis of a
set of benchmark astrophysical sources.
