Classification and grade prediction of aviation accident based on Lasso-SVR model

Long Zhao; Jixing Chen; Lirun Zhang; Mingyu Ma; Yu Wan; Hongyan Fang

doi:10.1117/12.3011662

7 December 2023 Classification and grade prediction of aviation accident based on Lasso-SVR model

Long Zhao, Jixing Chen, Lirun Zhang, Mingyu Ma, Yu Wan, Hongyan Fang

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129413K (2023) https://doi.org/10.1117/12.3011662
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In aviation accident classification and prediction, there are numerous factors that contribute to accidents, and these factors have intricate relationships, making it difficult to classify accident types accurately. Current classification methods for aviation accidents face issues like inadequate data processing and failure to eliminate invalid samples, resulting in complex data and low accuracy. To address these problems, a classification method called Lasso SVR is proposed for aviation accident inducing factors. It aims to accurately classify aviation accident data. The method involves constructing an influential factor matrix containing eight representative factors and five accident grades. The Pearson correlation coefficient is then calculated to determine the data correlation, and dimensionality reduction is achieved using the Lasso minimum angle regression method. By optimizing the penalty coefficient, the weights of accident influencing factors are obtained. Support vector regression technology is employed to process the influential factor matrix and achieve accident classification, along with in-depth characteristic analysis. Experimental results show that the Lasso SVR model reduces computational complexity and data complexity, enabling faster approximation of linear relationships in the data. It performs well in processing aviation accident data, improving accuracy and providing better data fit compared to traditional SVR models. Additionally, the Lasso SVR model demonstrates superior performance, particularly in terms of fitting linear kernel functions, when compared to other kernel functions.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Long Zhao, Jixing Chen, Lirun Zhang, Mingyu Ma, Yu Wan, and Hongyan Fang "Classification and grade prediction of aviation accident based on Lasso-SVR model", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129413K (7 December 2023); https://doi.org/10.1117/12.3011662

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KEYWORDS

Data modeling

Education and training

Matrices

Data processing

Performance modeling

Safety

Statistical modeling

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