Classifications of Fermi-LAT unassociated sources in multiple machine learning methods

The classifications of Fermi-LAT unassociated sources are studied using multiple machine learning (ML) methods. The update data from 4FGL-DR3 are divided into high Galactic latitude (HGL, Galactic latitude $|b|>10^\circ$) and low Galactic latitude (LGL, $|b|\le10^\circ$) regions. In the HGL region, a voting ensemble of four binary ML classifiers achieves a 91$\%$ balanced accuracy. In the LGL region, an additional Bayesian-Gaussian (BG) model with three parameters is introduced to eliminate abnormal soft spectrum AGNs from the training set and ML-identified AGN candidates, a voting ensemble of four ternary ML algorithms reach an 81$\%$ balanced accuracy. And then, a catalog of Fermi-LAT all-sky unassociated sources is constructed. Our classification results show that (i) there are 1037 AGN candidates and 88 pulsar candidates with a balanced accuracy of $0.918 \pm 0.029$ in HGL region, which are consistent with those given in previous all-sky ML approaches; and (ii) there are 290 AGN-like candidates, 135 pulsar-like candidates, and 742 other-like candidates with a balanced accuracy of $0.815 \pm 0.027$ in the LGL region, which are different from those in previous all-sky ML approaches. Additionally, different training sets and class weights were tested for their impact on classifier accuracy and predicted results. The findings suggest that while different training approaches can yield similar model accuracy, the predicted numbers across different categories can vary significantly. Thus, reliable evaluation of the predicted results is deemed crucial in the ML approach for Fermi-LAT unassociated sources.