  Context. The North Ecliptic Pole (NEP) field provides a unique set of
panchromatic data, well suited for active galactic nuclei (AGN) studies.
Selection of AGN candidates is often based on mid-infrared (MIR) measurements.
Such method, despite its effectiveness, strongly reduces a catalog volume due
to the MIR detection condition. Modern machine learning techniques can solve
this problem by finding similar selection criteria using only optical and
near-infrared (NIR) data. Aims. Aims of this work were to create a reliable AGN
candidates catalog from the NEP field using a combination of optical SUBARU/HSC
and NIR AKARI/IRC data and, consequently, to develop an efficient alternative
for the MIR-based AKARI/IRC selection technique. Methods. A set of supervised
machine learning algorithms was tested in order to perform an efficient AGN
selection. Best of the models were formed into a majority voting scheme, which
used the most popular classification result to produce the final AGN catalog.
Additional analysis of catalog properties was performed in form of the spectral
energy distribution (SED) fitting via the CIGALE software. Results. The
obtained catalog of 465 AGN candidates (out of 33 119 objects) is characterized
by 73% purity and 64% completeness. This new classification shows consistency
with the MIR-based selection. Moreover, 76% of the obtained catalog can be
found only with the new method due to the lack of MIR detection for most of the
new AGN candidates. Training data, codes and final catalog are available via
the github repository. Final AGN candidates catalog will be also available via
the CDS service after publication.
