基于LightGBM的航班延误多分类预测

Multi-classification Prediction of Flight Delay Based on LightGBM

航班延误是民航业的一大难题,提前对航班的延误情况进行预测,以采取合理的应对措施,对缓解航班延误产生的负面影响有着重要意义。为提升预测性能,提出一种基于轻量级梯度提升机(Light gradient boosting machine,LightGBM)的航班延误多分类预测模型。该模型结合航班信息与天气信息,运用方差过滤与递归特征消除进行特征筛选,并采用合成少数过采样技术(Synthetic minority oversampling technique,SMOTE)与Tomek Link对数据进行不平衡处理,最后使用LightGBM进行建模,实现对航班延误时长的多分类预测。为验证模型的合理性,将所提模型与其他先进算法构建的模型进行对比。实验结果表明,所提模型在各种预测性能指标上结果更优,将预测精度提升至90%以上,同时大幅度降低了训练时间成本。

Delay of flights is a core problem in civil aviation industry. It is significant to predict flight delay situation in advance so as to take reasonable measures to negative effects of flight delay. In order to improve the prediction performance, a flight delay multi-classification prediction model based on light gradient boosting machine(LightGBM)is put forward in this paper. This model can screen features by using variance filtering and recursive feature elimination according to flight information and weather information. It uses synthetic minority oversampling technique(SMOTE) and Tomek Link to deal with unbalanced data.Finally,LightGBM is used to build models,and multi-classification prediction of flight delay lengths can be realized. In order to verify the rationality of the model,this paper compares the proposed model with models constructed by other advanced algorithms. The experimental results show that the proposed model performs better in terms of various prediction performance indexes and can improve the prediction accuracy to 90% or higher. The model can also greatly reduce the training time and cost.