SIMBAD references

Significant progress in the classification of Fermi unassociated sources has led to an increase in the number of blazars being found. The optical spectrum is effectively used to classify the blazars into two groups such as BL Lac objects and flat spectrum radio quasars (FSRQs). However, the accurate classification of the blazars without optical spectrum information, i.e., blazars of uncertain type (BCUs), remains a significant challenge. In this paper, we present a principle component analysis (PCA) and machine-learning hybrid blazars classification method. The method, based on the data from the Fermi-LAT 3FGL Catalog, first used the PCA to extract the primary features of the BCUs and then used a machine-learning algorithm to further classify the BCUs. Experimental results indicate that the use of PCA algorithms significantly improved the classification. More importantly, comparison with the Fermi-LAT 4FGL Catalog, which contains the spectral classification of those BCUs in the Fermi-LAT 3FGL Catalog, reveals that the proposed classification method in the study exhibits higher accuracy than currently established methods; specifically, 151 out of 171 BL Lac objects and 19 out of 24 FSRQs are correctly classified.