  The automated classification of objects from large catalogues or survey
projects is an important task in many astronomical surveys. Faced with various
classification algorithms, astronomers should select the method according to
their requirements. Here we describe several kinds of decision trees for
finding active objects by multi-wavelength data, such as REPTree, Random Tree,
Decision Stump, Random Forest, J48, NBTree, AdTree. All decision tree
approaches investigated are in the WEKA package. The classification performance
of the methods is presented. In the process of classification by decision tree
methods, the classification rules are easily obtained, moreover these rules are
clear and easy to understand for astronomers. As a result, astronomers are
inclined to prefer and apply them, thus know which attributes are important to
discriminate celestial objects. The experimental results show that when various
decision trees are applied in discriminating active objects (quasars, BL Lac
objects and active galaxies) from non-active objects (stars and galaxies),
ADTree is the best only in terms of accuracy, Decision Stump is the best only
considering speed, J48 is the optimal choice considering both accuracy and
speed.
