Prediction of Warning Level in Aircraft Accidents using Classification Techniques: An Empirical Study

This paper focuses on evaluation of risk and safety in civil aviation industry. There is a huge amount of knowledge and data aggregation in Aviation Company. This paper aims to study the performance of different classification algorithms on accident reports of the Federal Aviation Administration (FAA) Accident/incident Data System database, contains number of accident data records for all categories of aviation between the years of 1950 to 2012. The classification algorithms such as DT, KNN, SVM, NN, and NB are used to predict the warning level of the component as the class attribute. We have explored the use of different classification techniques on aviation components data. The rules construct are proved in terms of their accuracy and these results are seen to be very meaningful. This study also proved that the NB classifiers will performance better than other classifiers on airline data. This work may be useful for Aviation Company to make better prediction.

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