Data mining application on aviation accident data for predicting topmost causes for accidents

Abstract

Aviation safety management system is a vital component of the aviation industry. Aviation safety inspectors apply a broad knowledge about aviation industry, aviation safety, and the central laws and regulations, and strategies affecting aviation. In addition, they put on severe technical knowledge and skill in the operation and maintenance of aircraft. Data mining methods also have been successfully applied in aviation safety management system. Aviation industry accumulates large amount of knowledge and data. This paper proposes a method that applied data mining technique on the accident reports of the Federal Aviation Administration (FAA) accident/incident data system database which contains accident data records for all categories of civil aviation between the years of 1919 and 2014. In this study, we have investigated the application of several data mining methods on the accidents reports, to arrive at new inferences that could help aviation management system. Moreover correlation based feature selection (CFS) with Oscillating Search Technique is used to select the number of prominent attributes that are potential factors causing maximum number of accidents in aircraft. The principle of this work is to find out the effective attributes in order to reduce the number of the accidents in the aviation industry. This proposed novel idea named "improved oscillated correlation feature selection (IOCFS)" is evaluated against the conventional classifiers like Naive bayes, support vector machine (SVM), artificial neural network (ANN), k-nearest neighbor (k-NN), Multiclass classifier and decision tree (J48). The selected features are tested in terms of their accuracy, running time and reliability as in terms of true positive rate, false positive rate, precision, recall-measure and ROC. The results are seen to be the best for k-NN classifier on comparing with other conventional classifiers, with the value of k = 5.