NAOC Open IR
A hybrid ensemble method for pulsar candidate classification
Wang, Y.1,2; Pan, Z.3,4,5; Zheng, J.1,2; Qian, L.3,4,5; Li, M.1,2
2019-08-01
Source PublicationASTROPHYSICS AND SPACE SCIENCE
ISSN0004-640X
Volume364Issue:8Pages:13
AbstractIn 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 integrated pulse profile, chi-squared value for sine-squared fit to amended profile and best S/N value play important roles in Random Forest, while the skewness of the integrated pulse profile is one of the most significant features in XGBoost. More than 20 features were selected by their relative scores and then applied in three ensemble methods. In the Hybrid Ensemble method, we combined Random Forest and XGBoost with EasyEnsemble. By changing thresholds, we tried to make a trade-off between Recall and Precision to make them approximately equal and as high as possible. Experiments on HTRU 1 and HTRU 2 datasets show that the Hybrid Ensemble method achieves higher Recall than the other two algorithms. In HTRU 1 dataset, Recall, Precision, and F-Score of the Hybrid Ensemble method are 0.967, 0.971, and 0.969, respectively. In HTRU 2 dataset, the three values of that are 0.920, 0.917, and 0.918, respectively.
KeywordPulsars general Methods statistical Methods data analysis
DOI10.1007/s10509-019-3602-4
WOS KeywordSELECTION ; DISCOVERY ; SEARCHES
Language英语
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000483869900001
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/27732
Collection中国科学院国家天文台
Corresponding AuthorLi, M.
Affiliation1.Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 10004, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Beijing 10012, Peoples R China
4.Chinese Acad Sci, Ctr Astron Mega Sci, Beijing 100012, Peoples R China
5.Chinese Acad Sci, NAOC, CAS Key Lab FAST, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Wang, Y.,Pan, Z.,Zheng, J.,et al. A hybrid ensemble method for pulsar candidate classification[J]. ASTROPHYSICS AND SPACE SCIENCE,2019,364(8):13.
APA Wang, Y.,Pan, Z.,Zheng, J.,Qian, L.,&Li, M..(2019).A hybrid ensemble method for pulsar candidate classification.ASTROPHYSICS AND SPACE SCIENCE,364(8),13.
MLA Wang, Y.,et al."A hybrid ensemble method for pulsar candidate classification".ASTROPHYSICS AND SPACE SCIENCE 364.8(2019):13.
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