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Prediction of warning level in aircraft accidents using data mining techniques

Published online by Cambridge University Press:  27 January 2016

A. B. Arockia Christopher  and
S. Appavu alias Balamurugan
A. B. Arockia Christopher*
Affiliation:
Anna University, Chennai, India
S. Appavu alias Balamurugan*
Affiliation:
KLN College of Information Technology, Sivagangai, India
*
abchristomca@yahoo.com
app_s@yahoo.com
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Abstract

Data mining is a data analysis process which is designed for large amounts of data. It proposes a methodology for evaluating risk and safety and describes the main issues of aircraft accidents. We have a huge amount of knowledge and data collection in aviation companies. This paper focuses on different feature selectwindion techniques applied to the datasets of airline databases to understand and clean the dataset. CFS subset evaluator, consistency subset evaluator, gain ratio feature evaluator, information gain attribute evaluator, OneR attribute evaluator, principal components attribute transformer, ReliefF attribute evaluatoboundar and symmetrical uncertainty attribute evaluator are used in this study in order to reduce the number of initial attributes. 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. For this purpose Weka software tools are used. This study also proves that the principal components attribute with decision tree classifier would perform better than other attributes and techniques on airline data. Accuracy is also very highly improved. This work may be useful for an aviation company to make better predictions. Some safety recommendations are also addressed to airline companies.

Type
Research Article
Information
The Aeronautical Journal , Volume 118 , Issue 1206 , August 2014 , pp. 935 - 952
Copyright
Copyright © Royal Aeronautical Society 2014 

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