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arXiv 0802.0537astro-ph
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基于支持向量机和Kd树的类星体分类

Support Vector Machines and Kd-tree for Separating Quasars from Large Survey Databases

Gao Dan, Zhang Yanxia, Zhao Yongheng

摘要:

我们比较了两种自动分类算法的性能:k维树(kd树)和支持向量机(SVM),从斯隆数字巡天(SDSS)和两微米全天巡天(2 MASS)目录的数据库中的恒星中分离类星体。这两种算法在SDSS和2 MASS对象的子集上进行训练,其性质通过光谱学已知。我们选择不同的属性组合作为输入模式,训练分类器,只使用光度数据,并给出了这两种方法得到的分类结果。通过计算查准率和查全率、真阳性率和真阴性率、F-测度、G-均值和加权准确率等性能指标来评价两种算法的性能。研究表明,kd树和支持向量机都是有效的自动分类算法。支持向量机的精度略高,但kd树需要更少的计算时间。给定不同的输入模式的基础上的各种参数(如幅度,颜色信息),我们得出结论,kd树和支持向量机表现出更好的性能与更少的功能。此外,我们的研究结果还表明,使用四种颜色(u-g,g-r,r-i,i-z)和基于SDSS模型震级的r震级的准确性最高。通过kd树和支持向量机训练的分类器可用于解决虚拟天文台(VO)面临的自动分类问题;此外,它们都可用于大型调查项目中类星体候选者的光度预选,以优化望远镜的效率。

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 databases of the Sloan Digital Sky Survey (SDSS) and the Two Micron All Sky Survey (2MASS) catalogs. 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, color 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 colors (u-g, g-r, r-i, 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 all can be applied for the photometric preselection of quasar candidates for large survey projects in order to optimize the efficiency of telescopes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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