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Predicting the Habitability of Exoplanets based on GBRT Algorithm
ZHU Weijun 1 *,WANG Xin 2
1.School of Information Engineering, Zhengzhou University, zhengzhou, 450001, China;School of Information Engineering, Zhengzhou University, zhengzhou, 450001, China
2.
*Correspondence author
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Funding: NSFC（No.U1204608）
Opened online: 8 June 2018
Accepted by: none
Citation: ZHU Weijun,WANG Xin.Predicting the Habitability of Exoplanets based on GBRT Algorithm[OL]. [ 8 June 2018] http://en.paper.edu.cn/en_releasepaper/content/4745364
 
 
The habitability of exoplanets is a hot research topic in the field of the exploration of the universe in recent years. The Machine Learning (ML) technique provides a viable means for classifying exoplanets according to their habitability. However, the existing ML-based approaches of habitability classification have some serious shortcomings and limitations. To this end, we provide a novel method for predicting the habitability of exoplanet based on Gradient Boosted Regression Trees (GBRT). First, the physical and astronomical data on the potentially habitable exoplanets and the inhabitable ones are employed to train by algorithm GBRT. Then, the trained model is used to predict the habitability of the exoplanets in our test set. The simulated experimental results show that the predictive accuracy of the new method is as high as 100 %.
Keywords:Artifical Intelligence; Gradient Boosted Regression Trees; Exoplanet; Habitability; Binary Classification
 
 
 

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