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Proceedings Paper

Separating quasars from stars by support vector machines
Author(s): Yanxia Zhang; Hongwen Zheng; Yongheng Zhao
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Paper Abstract

Based on survey databases from different bands, we firstly employed random forest approach for feature selection and feature weighting, and investigated support vector machines (SVMs) to classify quasars from stars. Two sets of data were used, one from SDSS, USNO-B1.0 and FIRST (short for FIRST sample), and another from SDSS, USNO-B1.0 and ROSAT (short for ROSAT sample). The classification results with different data were compared. Moreover the SVM performance with different features was presented. The experimental result showed that the accuracy with FIRST sample was superior to that with ROSAT sample, in addition, when compared to the result with original features, the performance using selected features improved and that using weighted features decreased. Therefore we consider that while SVMs is applied for classification, feature selection is necessary since this not only improves the performance, but also reduces the dimensionalities. The good performance of SVMs indicates that SVMs is an effective method to preselect quasar candidates from multiwavelength data.

Paper Details

Date Published: 19 July 2010
PDF: 8 pages
Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402Z (19 July 2010); doi: 10.1117/12.856828
Show Author Affiliations
Yanxia Zhang, National Astronomical Observatories (China)
Hongwen Zheng, North China Electric Power Univ. (China)
Yongheng Zhao, National Astronomical Observatories (China)

Published in SPIE Proceedings Vol. 7740:
Software and Cyberinfrastructure for Astronomy
Nicole M. Radziwill; Alan Bridger, Editor(s)

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