论文标题
态度的伽马射线源概率多类别分类
Towards probabilistic multiclass classification of gamma-ray sources
论文作者
论文摘要
机器学习算法已用于确定非相关来源的概率分类。通常考虑将分类分为两个大型类别，例如银河系和半乳酸。但是，还有更多的物理资源类别。例如，最新的Fermi-LAT 4FGL-DR3目录中有23个类。在本说明中，鉴于伽马射线源的多类分类，我们将其中一个大类细分为两个子类。三个大型类中的每一个仍然包含几个物理类。我们将分类的性能分为两个和三个类。我们计算两类分类的接收器操作特征曲线，在三个类中，我们将子类的概率总和，以便获得两个大型类的类概率。我们还比较了两类和三类案例中的精度，召回和可靠性图。
Machine learning algorithms have been used to determine probabilistic classifications of unassociated sources. Often classification into two large classes, such as Galactic and extra-galactic, is considered. However, there are many more physical classes of sources. For example, there are 23 classes in the latest Fermi-LAT 4FGL-DR3 catalog. In this note we subdivide one of the large classes into two subclasses in view of a more general multi-class classification of gamma-ray sources. Each of the three large classes still encompasses several of the physical classes. We compare the performance of classifications into two and three classes. We calculate the receiver operating characteristic curves for two-class classification, where in case of three classes we sum the probabilities of the sub-classes in order to obtain the class probabilities for the two large classes. We also compare precision, recall, and reliability diagrams in the two- and three-class cases.
