NSSC OpenIR
Knowledge discovery of telemetry data cross-correlation structure based on ensemble learning
Alternative Title20200608134832;基于集成学习的遥测数据互相关结构知识发现
Shi, Mengxin1,2; Zhi, Jia1; Gao, Xiang1,2; Yang, Jiasen1
Source PublicationBeijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics
2020
Volume46Issue:1Pages:181-188
DOI10.13700/j.bh.1001-5965.2019.0137
ISSN10015965
Language中文
KeywordCorrelation 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
AbstractAimed 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 ByEI ; CSCD
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7491
Collection中国科学院国家空间科学中心
Affiliation1.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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