Study of Star/Galaxy Classification Based on the XGBoost Algorithm |
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| Institution: | 1. College of Information Science and Technology, Beijing Normal University, Beijing, China;2. School of Information Science and Engineering, Qufu Normal University, Beijing, China;3. Academy of Military Science of PLA, Beijing, China;4. State Key Laboratory on Microwave and Digital Communications, National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China;5. Business School, Beijing Normal University, Beijing, China;6. Faculty of Electrical Engineering, University of Liubljana, Tr?a?ka 25, 1000 Ljubljana, Slovenia;1. Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA;2. Department of Health Statistics, Weifang Medical University, Weifang, Shandong 261053, China |
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| Abstract: | Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement. |
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| Keywords: | Stars: fundamental parameters—galaxies: fundamental parameters—techniques: photometric—methods: data analysis |
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