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Proceedings Paper

Comparison of several algorithms for celestial object classification
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Paper Abstract

We present a comparative study of implementation of supervised classification algorithms on classification of celestial objects. Three different algorithms including Linear Discriminant Analysis (LDA), K-Dimensional Tree (KD-tree), Support Vector Machines (SVMs) are used for classification of pointed sources from the Sloan Digital Sky Survey (SDSS) Data Release Seven. All of them have been applied and tested on the SDSS photometric data which are filtered by stringent conditions to make them play the best performance. Each of six performance metrics of SVMs can achieve very high performance (99.00%). The performances of KD-tree are also very good since six metrics are over 97.00%. Although five metrics are more than 90.00%, the performances of LDA are relatively poor because the accuracy of positive prediction only reaches 85.98%. Moreover, we discuss what input pattern is the best combination of different parameters for the effectiveness of these methods, respectively.

Paper Details

Date Published: 19 July 2010
PDF: 10 pages
Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402M (19 July 2010); doi: 10.1117/12.856369
Show Author Affiliations
Nanbo Peng, National Astronomical Observatories (China)
Yanxia Zhang, National Astronomical Observatories (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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