Support vector machines and kd-tree for separating quasars from large survey data bases
文献类型：期刊论文
| 作者 | Gao, Dan1,2; Zhang, Yan-Xia1 ; Zhao, Yong-Heng1
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| 刊名 | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
 |
| 出版日期 | 2008-05-21 |
| 卷号 | 386期号:3页码:1417-1425 |
| 关键词 | methods : data analysis methods : statistical astronomical data bases : miscellaneous catalogues surveys quasars : general |
| 英文摘要 | We compare the performance of two automated classification algorithms, k-dimensional tree (kd-tree) and support vector machines (SVMs), to separate quasars from stars in the data bases of the Sloan Digital Sky Survey (SDSS) and the Two-Micron All Sky Survey (2MASS) catalogues. The two algorithms are trained on subsets of SDSS and 2MASS objects whose nature is known via spectroscopy. We choose different attribute combination as input patterns to train the classifier using photometric data only and present the classification results obtained by these two methods. Performance metrics, such as precision and recall, true positive rate and true negative rate, F-measure, G-mean and Weighted Accuracy, are computed to evaluate the performance of the two algorithms. The study shows that both kd-tree and SVMs are effective automated algorithms to classify point sources. SVMs show slightly higher accuracy, but kd-tree requires less computation time. Given different input patterns based on various parameters (e.g. magnitudes, colour information), we conclude that both kd-tree and SVMs show better performance with fewer features. What is more, our results also indicate that the accuracy using the four colours (u - g, g - r, r - i and i - z) and r magnitude based on SDSS model magnitudes adds up to the highest value. The classifiers trained by kd-tree and SVMs can be used to solve the automated classification problems faced by the virtual observatory (VO); moreover, they can all be applied for the photometric preselection of quasar candidates for large survey projects in order to optimise the efficiency of telescopes. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000255464900021 |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/7335]  |
| 专题 | 国家天文台_光学天文研究部 |
| 作者单位 | 1.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China 2.Grad Univ, Chinese Acad Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Dan,Zhang, Yan-Xia,Zhao, Yong-Heng. Support vector machines and kd-tree for separating quasars from large survey data bases[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2008,386(3):1417-1425. |
| APA | Gao, Dan,Zhang, Yan-Xia,&Zhao, Yong-Heng.(2008).Support vector machines and kd-tree for separating quasars from large survey data bases.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,386(3),1417-1425. |
| MLA | Gao, Dan,et al."Support vector machines and kd-tree for separating quasars from large survey data bases".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 386.3(2008):1417-1425. |
入库方式： OAI收割
来源：国家天文台
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