  In this work, Machine Learning (ML) methods are used to efficiently identify
the unassociated sources and the Blazar Candidate of Uncertain types (BCUs) in
the Fermi-LAT Third Source Catalog (3FGL). The aims are twofold: 1) to
distinguish the Active Galactic Nuclei (AGNs) from others (non-AGNs) in the
unassociated sources; 2) to identify BCUs into BL Lacertae objects (BL Lacs) or
Flat Spectrum Radio Quasars (FSRQs). Two dimensional reduction methods are
presented to decrease computational complexity, where Random Forest (RF),
Multilayer Perceptron (MLP) and Generative Adversarial Nets (GAN) are trained
as individual models. In order to achieve better performance, the ensemble
technique is further explored. It is also demonstrated that grid search method
is of help to choose the hyper-parameters of models and decide the final
predictor, by which we have identified 748 AGNs out of 1010 unassociated
sources, with an accuracy of 97.04%. Within the 573 BCUs, 326 have been
identified as BL Lacs and 247 as FSRQs, with an accuracy of 92.13%.
