An Ensemble Classification Model Based on Imbalanced Data for Aviation Safety

作     者：NI Xiaomei WANG Huawei LV Shaolan XIONG Minglan NI Xiaomei;WANG Huawei;LV Shaolan;XIONG Minglan

作者机构：Civil Aviation CollegeNanjing University of Aeronautics and AstronauticsNanjing 21106JiangsuChina 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2021年第26卷第5期

页      面：437-443页

核心收录：

学科分类：08[工学] 0837[工学-安全科学与工程] 

基　　金：Supported by the Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China (U1833110) 

主　　题：aviation safety Aviation Safety Reporting System(ASRS) ensemble model imbalance data classification Light Gradient Boosting Machine(LGBM) 

摘      要：Nowadays aviation accidents have become one of the major causes of severe injuries and fatalities around the world. This attracts the research community to look into aviation safety by applying data analysis techniques based on an advanced machine learning algorithm. An ensemble classification model based on Aviation Safety Reporting System(ASRS) has been proposed to analyze aviation safety targeting the people injured in the *** ensemble classification model shall contain two modules: the data-driven module consisting of data cleaning, feature selection,and imbalanced data division and reorganization, and the modeldriven module stacked by Random Forest(RF), XGBoost(XGB),and Light Gradient Boosting Machine(LGBM) separately. The results indicate that the ensemble model could solve the data imbalance while vastly improving accuracy. LGBM illustrates higher accuracy and faster run in the analysis of a single model of the ASRS-based imbalanced data, while the ensemble model has the best performance in classification at the same time. The ensemble model proposed for imbalanced data classification can provide a certain reference for similar data processing while improving the safety of civil aviation.