Separating quasars from stars by support vector machines

Yanxia Zhang; Hongwen Zheng; Yongheng Zhao

doi:10.1117/12.856828

19 July 2010 Separating quasars from stars by support vector machines

Yanxia Zhang, Hongwen Zheng, Yongheng Zhao

Proceedings Volume 7740, Software and Cyberinfrastructure for Astronomy; 77402Z (2010) https://doi.org/10.1117/12.856828
Event: SPIE Astronomical Telescopes + Instrumentation, 2010, San Diego, California, United States

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.

Citation Download Citation

Yanxia Zhang, Hongwen Zheng, and Yongheng Zhao "Separating quasars from stars by support vector machines", Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402Z (19 July 2010); https://doi.org/10.1117/12.856828

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