Recognition of Air Passengers' Willingness to Pay for Seat Selection for Imbalanced Data Based on Improved XGBoost

Abstract

Passenger-paid seat selection is one of the important sources of ancillary revenue for airlines, and machine learning-based willingness-to-pay identification is of great practicality for airlines to accurately tap potential willing passengers. However, affected by periodic statistical errors, air passenger order data often has some problems such as high noise, high latitude, and unbalanced category. In view of this, this paper proposes a method for identifying air passengers' willingness to pay for seat selection based on improved XGBoost, which is improved and integrated from three stages: data, feature, and algorithm. The feasibility of the proposed multi-stage improved integration method is verified by real airline passenger dataset, and the experimental results show that the proposed improved method has better classification effect when compared with the classical six imbalance classification models, which provides a basis for accurate marketing of airline paid seat selection programs.