Published online by Cambridge University Press: 03 June 2010
We investigate selection and weighting of features by applying a random forest algorithm to multiwavelength data. Then we employ a k-nearest neighbor method to distinguish quasars from stars. We then compare the performance of this approach based on all features, weighted features, and selected features. We find that the k-nearest neighbor approach combined with random forests effectively separates quasars from stars.