Knowledge discovery of telemetry data cross-correlation structure based on ensemble learning

doi:10.13700/j.bh.1001-5965.2019.0137

NSSC OpenIR

	Knowledge discovery of telemetry data cross-correlation structure based on ensemble learning
Alternative Title	20200608134832;基于集成学习的遥测数据互相关结构知识发现
	Shi, Mengxin 1,2; Zhi, Jia 1; Gao, Xiang 1,2; Yang, Jiasen 1
Source Publication	Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics
	2020
Volume	46 Issue:1 Pages:181-188
DOI	10.13700/j.bh.1001-5965.2019.0137
ISSN	10015965
Language	中文
Keyword	Correlation methods - Learning systems - Matrix algebra - Telemetering equipment Classification accuracy - Cost-sensitive - Gradient boosting - Neural network ensembles - Performance indicators - Receiver operating characteristic curves - Structural information - Telemetry data
Abstract	Aimed at the problem that traditional telemetry data correlation analysis methods can only discover relevant degree knowledge and cannot provide relevant structural information, an extreme gradient boosting (XGBoost) and neural network ensemble learning method is proposed to discover the cross-correlation structural knowledge of telemetry data. Based on the dimension related structural information annotated by linearity, monotony, order pair consistency and scatter diagram shape, an algorithm combining hybrid sampling, cost sensitive matrix, neural network and XGBoost is developed to directly measure the telemetry data. The data is classified to obtain knowledge of relevant structural categories or related relationships. The results of experiments using quantum satellite mission data indicate that compared with the original XGBoost model, and the fusion-mixed sampling and cost-sensitive XGBoost model, the XGBoost model with neural network ensemble has higher classification accuracy on the performance indicators such as receiver operating characteristic (ROC) curve and F1-score. The proposed method is not sensitive to categorially imbalanced data, making it an effective method for the discovery of cross-correlation structural knowledge of telemetry data. © 2020, Editorial Board of JBUAA. All right reserved.
Indexed By	EI ; CSCD
Citation statistics
Document Type	期刊论文
Identifier	http://ir.nssc.ac.cn/handle/122/7491
Collection	中国科学院国家空间科学中心
Affiliation	1.National Space Science Center, Chinese Academy of Sciences, Beijing; 100190, China; 2.University of Chinese Academy of Sciences, Beijing; 100049, China
Recommended Citation GB/T 7714	Shi, Mengxin,Zhi, Jia,Gao, Xiang,et al. Knowledge discovery of telemetry data cross-correlation structure based on ensemble learning[J]. Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics,2020,46(1):181-188.
APA	Shi, Mengxin,Zhi, Jia,Gao, Xiang,&Yang, Jiasen.(2020).Knowledge discovery of telemetry data cross-correlation structure based on ensemble learning.Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics,46(1),181-188.
MLA	Shi, Mengxin,et al."Knowledge discovery of telemetry data cross-correlation structure based on ensemble learning".Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 46.1(2020):181-188.

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