Classification algorithms and applications for flight delay prediction based on unbalanced data

Abstract

By predicting flight delays, economic losses caused by delay situations can be effectively prevented and reduced. A classification algorithm for flight delay prediction based on unbalanced data is proposed to solve the problem of unbalanced number of delayed and non-delayed samples in the process of flight delay prediction. Firstly, a multi-classification model based on the CycleMLP algorithm is proposed for flight delay prediction. Secondly, the CycleMLP algorithm is improved by using the Focal Loss modified cross-entropy loss function to build the final classification model for flight delay prediction. And the experimental results show that the improved model has better prediction results for delayed flights, with a 5.54% increase in average macro precision and 1.86% increase in accuracy. Finally, the flight delay prediction results are published in the mobile APP application system to provide corresponding advice to passengers and airport departments.