The Third Fermi-LAT source catalog (3FGL) presents a large number of gamma-ray point sources affiliated with source types and counterparts.
Nonetheless, 1010 sources remain unassociated and 573 sources are associated with active galaxies of uncertain type.
Assigning blazar classes to these unassociated and uncertain sources, and linking counterparts to the unassociated ones, will refine tremendously our knowledge of the population of gamma-ray emitting objects.
To figure out the most likely counterpart, the sample of associated 3FGL sources is used to train machine learning classification algorithms.
For any particular 3FGL source, all possible combinations with measurements of one additional energy range are considered, e.g. from the Wide-Field Infrared Survey Explorer (WISE) source catalog, the catalog of Faint Images of the Radio Sky at Twenty cm (FIRST), or the Swift X-ray Point Source (1SXPS) catalog.
By merging the most probable candidates of each of those studies, the power of multiwavelength strategies is exploited and conclusions with even higher confidence concerning blazar counterpart candidates are drawn.
In this contribution, the statistical model and its validation to estimate the performance is described. Finally, results of the application of this novel wavelength-dependent approach are presented, and its consequences concerning blazar population studies are discussed.