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Algorithm Research & Explore
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3769-3772

Selective ensemble prediction model for gas disaster based on correlation analysis

Jia Pengtao
Lyu Qiaolin
College of Computer Science & Technology, Xi'an University of Science & Technology, Xi'an 710054, China

Abstract

In view of the low prediction performance of gas disaster risk prediction, this paper studied the prediction method of gas disaster selective ensemble regression learning based on correlation analysis of mine gas concentration and environmental factors. Firstly, this paper analyzed the correlation between gas concentration and sample attributes, and reduced the attribute to obtain a new data set according to the results of the correlation analysis. Secondly, it trained base learners, and used the optimization ensemble forward sequential selection method to establish the selective ensemble regression learning model. Finally, it used the model for gas disaster prediction. The experimental results show that the recognition rate of the proposed model for gas disaster risk compared with the four learner without correlation analysis improves 24% on average, and compared with the selective ensemble regression learning model without correlation analysis improves 7.6%.

Foundation Support

西安市科技计划资助项目(2017079CG/RC042(XAKD001))

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.08.0591
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 12
Section: Algorithm Research & Explore
Pages: 3769-3772
Serial Number: 1001-3695(2019)12-053-3769-04

Publish History

[2019-12-05] Printed Article

Cite This Article

贾澎涛, 吕巧林. 基于相关性分析的瓦斯灾害选择集成预测模型 [J]. 计算机应用研究, 2019, 36 (12): 3769-3772. (Jia Pengtao, Lyu Qiaolin. Selective ensemble prediction model for gas disaster based on correlation analysis [J]. Application Research of Computers, 2019, 36 (12): 3769-3772. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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