用机器学习自动分类航空事件严重程度，提升安全调查效率。
A Practical Approach to using Supervised Machine Learning Models to Classify Aviation Safety Occurrences
- 采用五种监督学习模型，基于公开报告数据训练分类器。
- 随机森林表现最佳，准确率77%，F1分数78%。
- 数据增强反而降低部分模型性能，需谨慎使用。
本文提出一种实用的监督学习方法，帮助安全调查人员将航空事件自动分类为一般事件或严重事件。该方法已部署为机器学习网络应用，基于公开可获取的航空事故调查报告构建标签数据集。评估了五种模型：支持向量机、逻辑回归、随机森林分类器、XGBoost和K近邻。结果显示，随机森林分类器表现最优，平均100次运行下准确率为0.77，F1分数为0.78，马修相关系数（MCC）为0.51。研究还探讨了对不平衡数据应用合成少数类过采样技术（SMOTE）的影响，结果表明其效果从无显著提升到部分模型性能明显下降不等。
原文摘要 · Abstract (English)
This paper describes a practical approach of using supervised machine learning (ML) models to assist safety investigators to classify aviation occurrences into either incident or serious incident categories. Our implementation currently deployed as a ML web application is trained on a labelled dataset derived from publicly available aviation investigation reports. A selection of five supervised learning models (Support Vector Machine, Logistic Regression, Random Forest Classifier, XGBoost and K-Nearest Neighbors) were evaluated. This paper showed the best performing ML algorithm was the Random Forest Classifier with accuracy = 0.77, F1 Score = 0.78 and MCC = 0.51 (average of 100 sample runs). The study had also explored the effect of applying Synthetic Minority Over-sampling Technique (SMOTE) to the imbalanced dataset, and the overall observation ranged from no significant effect to substantial degradation in performance for some of the models after the SMOTE adjustment.
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