  We give an overview of ISINA: INTEGRAL Source Identification Network
Algorithm. This machine learning algorithm, using Random Forests, is applied to
the IBIS/ISGRI dataset in order to ease the production of unbiased future soft
gamma-ray source catalogues. First we introduce the dataset and the problems
encountered when dealing with images obtained using the coded mask technique.
The initial step of source candidate searching is introduced and an initial
candidate list is created. A description of the feature extraction on the
initial candidate list is then performed together with feature merging for
these candidates. Three training and testing sets are created in order to deal
with the diverse timescales encountered when dealing with the gamma-ray sky.
Three independent Random Forest are built: one dealing with faint persistent
source recognition, one dealing with strong persistent sources and a final one
dealing with transients. For the latter, a new transient detection technique is
introduced and described: the Transient Matrix. Finally the performance of the
network is assessed and discussed using the testing set and some illustrative
source examples.
