  We present a classification-based approach to identify quasi-stellar radio
sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance
on a manually labeled training set. While reasonable results can already be
obtained via approaches working only on photometric data, our experiments
indicate that simple but problem-specific features extracted from spectroscopic
data can significantly improve the classification performance. Since our
approach works orthogonal to existing classification schemes used for building
the spectroscopic catalogs, our classification results are well suited for a
mutual assessment of the approaches' accuracies.
