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YanXia Zhang; YongHeng Zhao; Dan Gao
Source Publicationresearchinastronomyandastrophysics
AbstractWe 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.
Funding Project[National Natural Science Foundation of China] ; [863 project]
Document Type期刊论文
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
YanXia Zhang,YongHeng Zhao,Dan Gao. randomforestalgorithmforclassificationofmultiwavelengthdata[J]. researchinastronomyandastrophysics,2009,9(2):220.
APA YanXia Zhang,YongHeng Zhao,&Dan Gao.(2009).randomforestalgorithmforclassificationofmultiwavelengthdata.researchinastronomyandastrophysics,9(2),220.
MLA YanXia Zhang,et al."randomforestalgorithmforclassificationofmultiwavelengthdata".researchinastronomyandastrophysics 9.2(2009):220.
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