不同样本类星体光度红移估计的机器学习

Yanxia Zhang, Xin Jin, Jingyi Zhang, Yongheng Zhao
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引用次数: 1

摘要

我们比较了基于SDSS_WISE样本的支持向量机、XGBoost、LightGBM、k近邻、随机森林和Extra-Trees在类星体光度红移估计上的性能。对于这个样本,LightGBM在速度上表现出优势,而k-Nearest Neighbors, Random forests和Extra-Trees表现出更好的性能。然后在SDSS、SDSS_WISE、SDSS_UKIDSS、WISE_UKIDSS和SDSS_WISE_UKIDSS样本上应用k近邻、随机森林和Extra-Trees。结果表明,算法的性能取决于样本选择、样本大小、输入模式和不同波段的信息;对于同一样本,信息越多,性能越好,但不同算法的准确率不同;没有一种算法在所有样本上都表现出优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning for Photometric Redshift Estimation of Quasars with Different Samples
We compare the performance of Support Vector Machine, XGBoost, LightGBM, k-Nearest Neighbors, Random forests and Extra-Trees on the photometric redshift estimation of quasars based on the SDSS_WISE sample. For this sample, LightGBM shows its superiority in speed while k-Nearest Neighbors, Random forests and Extra-Trees show better performance. Then k-Nearest Neighbors, Random forests and Extra-Trees are applied on the SDSS, SDSS_WISE, SDSS_UKIDSS, WISE_UKIDSS and SDSS_WISE_UKIDSS samples. The results show that the performance of an algorithm depends on the sample selection, sample size, input pattern and information from different bands; for the same sample, the more information the better performance is obtained, but different algorithms shows different accuracy; no single algorithm shows its superiority on every sample.
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