  We present the results of various automated classification methods, based on
machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of
the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from
quasars. We provide a careful scrutiny of approaches available in the
literature and have highlighted the pitfalls in those approaches based on the
nature of data used for the study. The aim is to investigate the
appropriateness of the application of certain ML methods. The manuscript argues
convincingly in favor of the efficacy of asymmetric AdaBoost to classify
photometric data. The paper presents a critical review of existing study and
puts forward an application of asymmetric AdaBoost, as an offspring of that
exercise.
