A Practical Approach to using Supervised Machine Learning Models to Classify Aviation Safety Occurrences
This work addresses a practical problem for aviation safety investigators by automating classification, but it is incremental as it applies standard ML models to a new dataset.
The paper tackled classifying aviation safety occurrences as incidents or serious incidents using supervised machine learning, finding that Random Forest Classifier performed best with an accuracy of 0.77, F1 score of 0.78, and MCC of 0.51, while SMOTE had mixed effects.
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.