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Proceedings Paper

Automated classification of pointed sources
Author(s): Yanxia Zhang; Yongheng Zhao; Hongwen Zheng
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Paper Abstract

Facing very large and frequently high dimensional data in astronomy, effectiveness and efficiency of algorithms are always the hot issue. Excellent algorithms must avoid the curse of dimensionality and simultaneously should be computationally efficient. Adopting survey data from optical bands (SDSS, USNO-B1.0) and radio band (FIRST), we investigate feature weighting and feature selection by means of random forest algorithm. Then we employ a kd-tree based k-nearest neighbor method (KD-KNN) to discriminate quasars from stars. Then the performance of this approach based on all features, weighted features and selected features are compared. The experimental result shows that the accuracy improves when using weighted features or selected features. KD-KNN is a quite easy and efficient approach to nonparametric classification. Obviously KD-KNN combined with random forests is more effective to separate quasars from stars with multi-wavelength data.

Paper Details

Date Published: 19 July 2010
PDF: 8 pages
Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402S (19 July 2010); doi: 10.1117/12.856826
Show Author Affiliations
Yanxia Zhang, National Astronomical Observatories (China)
Yongheng Zhao, National Astronomical Observatories (China)
Hongwen Zheng, North China Electric Power Univ. (China)

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

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