Aviation Safety Ensemble Classification Model Based on Imbalanced Data

In recent times, aviation accidents have emerged as a significant contributor to severe injuries and fatalities globally. This has prompted the research community to explore aviation safety using advanced machine learning algorithms and data analysis techniques. To address the safety concerns in aviation, a novel ensemble classification model has been proposed, leveraging the Aviation Safety Reporting System (ASRS) data. The focus of this model is to analyze and assess the safety aspects pertaining to individuals affected within the aviation system. 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.