School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Department of Scientific Research, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Sci Rep. 2022 Jul 1;12(1):11174. doi: 10.1038/s41598-022-14566-3.
Airplanes have always been one of the first choices for people to travel because of their convenience and safety. However, due to the outbreak of the new coronavirus epidemic in 2020, the civil aviation industry of various countries in the world has encountered severe challenges. Predicting aircraft passenger satisfaction and excavating the main influencing factors can help airlines improve their services and gain advantages in difficult situations and competition. This paper proposes a RF-RFE-Logistic feature selection model to extract the influencing factors of passenger satisfaction. First, preliminary feature selection is performed using recursive feature elimination based on random forest (RF-RFE). Second, based on different classification models, KNN, logistic regression, random forest, Gaussian Naive Bayes, and BP neural network, the classification performance of the models before and after feature selection is compared, and the prediction model with the best classification performance is selected. Finally, based on the RF-RFE feature selection, combined with the logistic model, the factors affecting customer satisfaction are further extracted. The experimental results show that the RF-RFE model selects a feature subset containing 17 variables. In the classification prediction model, the random forest after RF-RFE feature selection shows the best classification performance. Finally, combined with the four important variables extracted by RF-RFE and logistic regression, further discussion is carried out, and suggestions are given for airlines to improve passenger satisfaction.
飞机因其便捷性和安全性，一直是人们出行的首选之一。然而，由于 2020 年新冠疫情的爆发，世界各国的民航业遭遇了严峻的挑战。预测飞机旅客满意度并挖掘主要影响因素，可以帮助航空公司改善服务，在困境和竞争中取得优势。本文提出了一种 RF-RFE-Logistic 特征选择模型，用于提取旅客满意度的影响因素。首先，基于随机森林（RF）的递归特征消除（RFE）进行初步特征选择。其次，基于不同的分类模型，如 KNN、逻辑回归、随机森林、高斯朴素贝叶斯和 BP 神经网络，比较了特征选择前后模型的分类性能，选择分类性能最佳的预测模型。最后，基于 RF-RFE 特征选择，结合逻辑模型，进一步提取影响客户满意度的因素。实验结果表明，RF-RFE 模型选择了一个包含 17 个变量的特征子集。在分类预测模型中，RF-RFE 特征选择后的随机森林表现出最佳的分类性能。最后，结合 RF-RFE 和逻辑回归提取的四个重要变量，进行了进一步的讨论，并为航空公司提高旅客满意度提出了建议。
