A hybrid ensemble method for pulsar candidate classification

• Published in: Astrophysics and space science Vol. 364; no. 8; pp. 1 - 13
• Main Authors: Wang, Y., Pan, Z., Zheng, J., Qian, L., Li, M.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2019; Springer Nature B.V
• Subjects: Algorithms; Astrobiology; Astronomy; Astrophysics; Astrophysics and Astroparticles; Cosmology; Datasets; Forests; Methods; Observations and Techniques; Original Article; Physics; Physics and Astronomy; Pulsars; Recall; Skewness; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Statistical tests; Methods: statistical; Pulsars: general; Methods: data analysis

In this paper, three ensemble methods: Random Forest, XGBoost, and a Hybrid Ensemble method were implemented to classify imbalanced pulsar candidates. To assist these methods, tree models were used to select features among 30 features of pulsar candidates from references. The skewness of the integra...