  We compare the performance of Bayesian Belief Networks (BBN), Multilayer
Perceptron (MLP) networks and Alternating Decision Trees (ADtree) on separating
quasars from stars with the database from the 2MASS and FIRST survey catalogs.
Having a training sample of sources of known object types, the classifiers are
trained to separate quasars from stars. By the statistical properties of the
sample, the features important for classification are selected. We compare the
classification results with and without feature selection. Experiments show
that the results with feature selection are better than those without feature
selection. From the high accuracy, it is concluded that these automated methods
are robust and effective to classify point sources, moreover they all may be
applied for large survey projects (e.g. selecting input catalogs) and for other
astronomical issues, such as the parameter measurement of stars and the
redshift estimation of galaxies and quasars.
