Selecting Quasar Candidates by a SVM Classification System
  We develop and demonstrate a classification system constituted by several
Support Vector Machines (SVM) classifiers, which can be applied to select
quasar candidates from large sky survey projects, such as SDSS, UKIDSS, GALEX.
How to construct this SVM classification system is presented in detail. When
the SVM classification system works on the test set to predict quasar
candidates, it acquires the efficiency of 93.21% and the completeness of
97.49%. In order to further prove the reliability and feasibility of this
system, two chunks are randomly chosen to compare its performance with that of
the XDQSO method used for SDSS-III's BOSS. The experimental results show that
the high faction of overlap exists between the quasar candidates selected by
this system and those extracted by the XDQSO technique in the dereddened i-band
magnitude range between 17.75 and 22.45, especially in the interval of
dereddened i-band magnitude < 20.0. In the two test areas, 57.38% and 87.15% of
the quasar candidates predicted by the system are also targeted by the XDQSO
method. Similarly, the prediction of subcategories of quasars according to
redshift achieves a high level of overlap with these two approaches. Depending
on the effectiveness of this system, the SVM classification system can be used
to create the input catalog of quasars for the GuoShouJing Telescope (LAMOST)
or other spectroscopic sky survey projects. In order to get higher confidence
of quasar candidates, cross-result from the candidates selected by this SVM
system with that by XDQSO method is applicable.
