| Citation: | FENG X，SANG X，ZUO H C. Prediction of aviation safety event risk level based on ensemble cost-sensitive deep neural network[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（4）：1117-1128 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0473
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One key component of active risk management is the prediction of aviation safety event risk levels.Considering the characteristics of high-dimensional complexity and class imbalance presented by massive aviation safety event data, this paper proposes an aviation safety event risk level prediction method based on an ensemble cost-sensitive deep neural network (ECSDNN). First, the feature representation of aviation safety event data is realized by using the method of splicing type attribute embedding coding and numerical attribute; secondly, a cost-sensitive matrix and a cost-sensitive loss function are designed comprehensively considering the misclassification ratio and fixed cost, and a base classifier model based on a cost-sensitive deep neural network (CSDNN) is constructed; finally, an ensemble prediction model of aviation safety event risk level ECSDNN is created by integrating various base classifiers with varying parameters and performances using the hard voting approach.The experimental results on the aviation safety reporting system (ASRS) dataset demonstrate that the prediction accuracy of the ECSDNN model is improved by 3.17% when compared with the single CSDNN base classifier and by 4.51% when compared with the optimal prediction ability of the benchmark algorithm.The effectiveness of the ensemble cost-sensitive deep neural network method for aviation safety event risk level prediction is verified.
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