Automated classification of pointed sources

Yanxia Zhang; Yongheng Zhao; Hongwen Zheng

doi:10.1117/12.856826

19 July 2010 Automated classification of pointed sources

Yanxia Zhang, Yongheng Zhao, Hongwen Zheng

Proceedings Volume 7740, Software and Cyberinfrastructure for Astronomy; 77402S (2010) https://doi.org/10.1117/12.856826
Event: SPIE Astronomical Telescopes + Instrumentation, 2010, San Diego, California, United States

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.

Citation Download Citation

Yanxia Zhang, Yongheng Zhao, and Hongwen Zheng "Automated classification of pointed sources", Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402S (19 July 2010); https://doi.org/10.1117/12.856826

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