Random forest algorithm for classification of multiwavelength data 被引量：3

Random forest algorithm for classification of multiwavelength data

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摘要 We introduced a decision tree method called Random Forests for multiwavelength data classification. The data were adopted from different databases, including the Sloan Digital Sky Survey （SDSS） Data Release five, USNO, FIRST and ROSAT. We then studied the discrimination of quasars from stars and the classification of quasars, stars and galaxies with the sample from optical and radio bands and with that from optical and X-ray bands. Moreover, feature selection and feature weighting based on Random Forests were investigated. The performances based on different input patterns were compared. The experimental results show that the random forest method is an effective method for astronomical object classification and can be applied to other classification problems faced in astronomy. In addition, Random Forests will show its superiorities due to its own merits, e.g. classification, feature selection, feature weighting as well as outlier detection. We introduced a decision tree method called Random Forests for multiwavelength data classification. The data were adopted from different databases, including the Sloan Digital Sky Survey （SDSS） Data Release five, USNO, FIRST and ROSAT. We then studied the discrimination of quasars from stars and the classification of quasars, stars and galaxies with the sample from optical and radio bands and with that from optical and X-ray bands. Moreover, feature selection and feature weighting based on Random Forests were investigated. The performances based on different input patterns were compared. The experimental results show that the random forest method is an effective method for astronomical object classification and can be applied to other classification problems faced in astronomy. In addition, Random Forests will show its superiorities due to its own merits, e.g. classification, feature selection, feature weighting as well as outlier detection.

作者 Dan Gao Yan-Xia Zhang Yong-Heng Zhao

机构地区 National Astronomical Observatories Graduate University of Chinese Academy of Sciences

出处《Chinese Journal of Astronomy and Astrophysics》 CSCD 2009年第2期220-226,共7页 中国天文和天体物理学报（英文版）

基金 Supported by the National Natural Science Foundation of China This paper is funded by the National Natural Science Foundation of China under grant under GrantNos. 10473013, 90412016 and 10778724 by the 863 project under Grant No. 2006AA01A120

关键词 classification-- astronomical databases miscellaneous -- catalogs -- meth- ods data analysis -- methods statistical classification-- astronomical databases miscellaneous -- catalogs -- meth- ods data analysis -- methods statistical

分类号 TP311.13 [自动化与计算机技术—计算机软件与理论] S782.1 [农业科学—木材科学与技术]

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