Spectroscopy and Spectral Analysis, Volume. 32, Issue 2, 510(2012)
Data Mining Approach to Cataclysmic Variables Candidates Based on Random Forest Algorithm
An automatic and efficient method for cataclysmic variables candidates is presented in the present paper. The identified CVs were selected as templates. A model was constructed by random forest algorithm with templates and random selected spectra. Wavelength ranking was described by the model and the classifier was constructed afterwards. Most of the non-candidates were excluded by the method. Template matching strategy was used to identify the final candidates which were analyzed to complement the templates as feedback. 16 new CVs candidates were found in the experiment that shows that our approach to finding special celestial bodies can be feasible in LAMOST.
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JIANG Bin, LUO A-li, ZHAO Yong-heng. Data Mining Approach to Cataclysmic Variables Candidates Based on Random Forest Algorithm[J]. Spectroscopy and Spectral Analysis, 2012, 32(2): 510
Received: Mar. 10, 2011
Accepted: --
Published Online: Feb. 20, 2012
The Author Email: Bin JIANG (jiangbin@sdu.edu.cn)