ABSTRACT
Improving performing and safety of the aircraft operation is one of the most important issues addressed by experts. Such improvement can result not only in the less frequent loss of equipment but primarily in the protection of health or saving lives of both crew members and others involved. Reducing such risks or minimizing impacts is possible by analyzing events, which had already occurred. In this paper, our main motivation consists in developing an effective and intelligent decision support system based on data mining techniques. In this context, data mining classifying algorithms with large datasets have been utilized to assess and analyse the risk factors statistically related to aircraft incidents in order to compare the performance of the implemented classifiers such as decision tree, discriminant and random forest. To underscore the practical cost, i.e., effectiveness of our approach, the selected classifiers have been implemented using statistical programming tools with datasets taken from the operation process. This analysis is expected to find the algorithm, which can support the decision taking.
