Journal
RESEARCH IN ASTRONOMY AND ASTROPHYSICS
Volume 9, Issue 2, Pages 220-226Publisher
NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
DOI: 10.1088/1674-4527/9/2/011
Keywords
classification; astronomical databases: miscellaneous; catalogs; methods: data analysis; methods: statistical
Categories
Funding
- National Natural Science Foundation of China [10473013, 90412016, 10778724]
- 863 project [2006AA01A120]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available