  Quasars behind the Galactic plane (GPQs) are important astrometric references
and useful probes of Milky Way gas. However, the search for GPQs is difficult
due to large extinctions and high source densities in the Galactic plane.
Existing selection methods for quasars developed using high Galactic latitude
(high-$b$) data cannot be applied to the Galactic plane directly because the
photometric data obtained from high-$b$ regions and the Galactic plane follow
different probability distributions. To alleviate this dataset shift problem
for quasar candidate selection, we adopt a Transfer Learning Framework at both
data and algorithm levels. At the data level, to make a training set in which
dataset shift is modeled, we synthesize quasars and galaxies behind the
Galactic plane based on SDSS sources and Galactic dust map. At the algorithm
level, to reduce the effect of class imbalance, we transform the three-class
classification problem for stars, galaxies, and quasars to two binary
classification tasks. We apply XGBoost algorithm on Pan-STARRS1 (PS1) and
AllWISE photometry for classification, and additional cut on Gaia proper motion
to remove stellar contaminants. We obtain a reliable GPQ candidate catalog with
160,946 sources located at $|b|\leq 20^{\circ}$ in PS1-AllWISE footprint.
Photometric redshifts of GPQ candidates achieved with XGBoost regression
algorithm show that our selection method can identify quasars in a wide
redshift range ($0<z\lesssim5$). This study extends the systematic searches for
quasars to the dense stellar fields and shows the feasibility of using
astronomical knowledge to improve data mining under complex conditions in the
Big Data era.
