A Practical Approach to using Supervised Machine Learning Models to Classify Aviation Safety Occurrences

• URL: http://arxiv.org/abs/2504.09063v1
• Date: Sat, 12 Apr 2025 03:46:33 GMT
• Title: A Practical Approach to using Supervised Machine Learning Models to Classify Aviation Safety Occurrences
• Authors: Bryan Y. Siow,
• Abstract summary: This paper describes a practical approach of using supervised machine learning (ML) models to classify aviation occurrences into either incident or serious incident categories.<n>Our implementation currently deployed as a ML web application is trained on a dataset derived from publicly available aviation investigation reports.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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