  In this second paper in a series of papers based on the most-up-to-date
catalogue of symbiotic stars (SySts), we present a new approach for identifying
and distinguishing SySts from other Halpha emitters in photometric surveys
using machine learning algorithms such as classification tree, linear
discriminant analysis, and K-nearest neighbour. The motivation behind of this
work is to seek for possible colour indices in the regime of near- and
mid-infrared covered by the 2MASS and WISE surveys. A number of diagnostic
colour-colour diagrams are generated for all the known Galactic SySts and
several classes of stellar objects that mimic SySts such as planetary nebulae,
post-AGB, Mira, single K and M giants, cataclysmic variables, Be, AeBe, YSO,
weak and classical T Tauri stars, and Wolf-Rayet. The classification tree
algorithm unveils that primarily J-H, W1-W4 and Ks-W3 and secondarily H-W2,
W1-W2 and W3-W4 are ideal colour indices to identify SySts. Linear discriminant
analysis method is also applied to determine the linear combination of 2MASS
and AllWISE magnitudes that better distinguish SySts. The probability of a
source being a SySt is determined using the K-nearest neighbour method on the
LDA components. By applying our classification tree model to the list of
candidate SySts (Paper I), the IPHAS list of candidate SySts, and the DR2
VPHAS+ catalogue, we find 125 (72 new candidates) sources that pass our
criteria while we also recover 90 per cent of the known Galactic SySts.
