Journal of System Simulation
Abstract
Abstract: Aiming at the classification of flight delay under imbalance data, a novel method based on nonlinear weighted XGBoost (extreme gradient boosting) is proposed. The imbalance of flight delay data and the influence for classification performance caused by the data imbalance are analyzed. A heuristic nonlinear weighting method based on sample proportion is proposed, and the negative log likelihood loss function is optimized. The real flight delay dataset is used to validate the performance of the classification algorithm. The experiment results show that the proposed nonlinear weighted XGBoost algorithm can improve the classification accuracy of flight delay, while ensuing a high overall classification accuracy. Compared to traditional methods, the proposed algorithm has good performance of statistical metrics and performance curves.
Recommended Citation
Hong, Tang; Dong, Wang; Bo, Song; Chu, Wenkui; and He, Linyuan
(2021)
"Classification of Flight Delay Based on Nonlinear Weighted XGBoost,"
Journal of System Simulation: Vol. 33:
Iss.
9, Article 27.
DOI: 10.16182/j.issn1004731x.joss.20-0372
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss9/27
First Page
2261
Revised Date
2020-08-05
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0372
Last Page
2269
CLC
U8;TP391.9
Recommended Citation
Tang Hong, Wang Dong, Song Bo, Chu Wenkui, He Linyuan. Classification of Flight Delay Based on Nonlinear Weighted XGBoost[J]. Journal of System Simulation, 2021, 33(9): 2261-2269.
DOI
10.16182/j.issn1004731x.joss.20-0372
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