Classification of Aviation Accidents Using Data Mining Algorithms

Abstract

Air transportation is a very preferred type of transportation for long-distance trips worldwide. This type of transportation has made great progress with the development of technology. In addition to its technological developments, passenger capacity is gradually increasing due to its fast and secure access. In contrast, the mortality rate is quite high in the case of an airplane accident, and hundreds of people die in a single accident. This research aims to classification several airplane accidents to find crucial factors and their overall impacts on the mentioned accident. In this study, appropriate data associated with said accidents worldwide since 2000 have been collected and then analyzed using sequential minimal optimization, decision tree (J48), and Naive Bayes. It is revealed that the decision tree algorithm provided the most accurate results for the study. Finally, appropriate comments were elaborated about each stage to reduce accidents. If these evaluations are taken into account, air transport will be much more reliable and thus loss of life will be minimized.

Keywords

• Aviation safety
• sequential minimal optimization
• decision tree
• Naive Bayes

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