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The automated classification of objects from large catalogs 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.  相似文献   
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We present results of optical follow-up observations of candidate ultra-luminous X-ray sources (ULXs). Using Keck optical spectroscopy, 17 of the candidates from the Colbert and Ptak [Colbert, E.J.M., Ptak, A.F. A catalog of candidate intermediate-luminosity X-ray objects. ApJS 143, 25–45, 2002] catalog have been identified; this is one of the largest sets of optical identifications of such objects thus far. Fifteen are background active galactic nuclei (AGN); two are foreground stars in our Galaxy. These findings are consistent with background and foreground object expectations, as derived from log N–log S relations. Also, the results are briefly discussed in terms of the spiral-galaxy/ULX connection.  相似文献   
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