Comparison of decision tree methods for finding active objects
365 citations recorded by the source corpus.
Abstract 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.
Subfields: Computational Mechanics, Instrumentation
View Paper: https://www.semanticscholar.org/paper/e6899ce6a4cc6c3153874222871f619b5c512047
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In Computational Mechanics
In Instrumentation
Citations in the corpus
- Landslide susceptibility modelling using different advanced decision trees methods (2018, methodology): “Zhao and Zhang (2008) reported that the ADT is the best method in terms of predictive accuracy compared to other decision trees (decision stump and random tree).”
- Random Forests and Decision Trees (2012, methodology): “As such, the decrease in bias [19] is achieved by the boosting method.”
- How Many Trees in a Random Forest? (2012, background): “Random Trees can be efficiently generated, and the combination of large sets of Random Trees generally lead to accurate models [35,10].”