Malyshev D, Bhat A (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: Gesellschaft fur Informatik (GI)
Book Volume: P-326
Pages Range: 479-488
Conference Proceedings Title: Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
Event location: Hamburg, DEU
ISBN: 9783885797203
DOI: 10.18420/inf2022_39
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 (23 classes in the latest Fermi-LAT 4FGL-DR3 catalog [Ab22]). 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.
APA:
Malyshev, D., & Bhat, A. (2022). Towards probabilistic multiclass classification of gamma-ray sources. In Daniel Demmler, Daniel Krupka, Hannes Federrath (Eds.), Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI) (pp. 479-488). Hamburg, DEU: Gesellschaft fur Informatik (GI).
MLA:
Malyshev, Dmitry, and Aakash Bhat. "Towards probabilistic multiclass classification of gamma-ray sources." Proceedings of the 2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 Computer Science in the Natural Sciences, INFORMATIK 2022, Hamburg, DEU Ed. Daniel Demmler, Daniel Krupka, Hannes Federrath, Gesellschaft fur Informatik (GI), 2022. 479-488.
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