| Citation: | SHI Mengxin, ZHI Jia, GAO Xiang, et al. Knowledge discovery of telemetry data cross-correlation structure based on ensemble learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(1): 181-188. doi: 10.13700/j.bh.1001-5965.2019.0137(in Chinese)
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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.
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