Improving the Classification of Airplane Accidents Severity using Feature Selection, Extraction and Machine Learning Models

Airplane mode of transportation is statistically the most secure means of travel. This is due to the fact that flights require several conditions and precautions because aviation accidents are most of the time fatal and have disastrous consequences. For this purpose, in this paper, the mean goal is to study the different levels of fatality of airplane accidents using machine learning models. The study rely on airplane accident severity dataset to implement three machine learning models: KNN, Decision Tree and Random Forest. This study began with implementing two features selection and extraction methods, PCA and RFE in order to reduce dataset dimensionality and complexity of models and reduce training time by implementing machine learning models on dataset and measuring their performance. Results show that KNN and Decision Tree demonstrates high levels of performances by achieving 100% of accuracy and f1-score metrics; while Random Forest achieves its best performances after application of PCA when it reaches an accuracy equal to 97.83% and f1-score equal to 97.82%.

@article{KAIDI2023,
  title     = {Improving the Classification of Airplane Accidents Severity using Feature Selection, Extraction and Machine Learning Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Rachid KAIDI and Mohammed AL ACHHAB and Mohamed LAZAAR and Hicham OMARA},
  doi       = {10.14569/IJACSA.2023.0141298},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141298}
}