Advertisement

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

  • A. B. Arockia ChristopherEmail author
  • S. Appavu alias Balamurugan
Conference paper
  • 741 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 243)

Abstract

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.

Keywords

Data mining Risk Safety KNN SVM Aviation 

Notes

Acknowledgments

The authors wish to acknowledge the financial support from the University Grant Commission (UGC), New Delhi, INDIA for the Major Research Project “Data Tuner for effective Data Pre-processing” vide reference F. No. 39-899/2010 (SR), and also gratefully acknowledge the unanimous reviewers for their kind suggestions and comments for improving this paper.

References

  1. 1.
    Chang, A.S., Leu, S.S.: Data mining model for identifying project profitability variables. Int. J. Project Manage. 24, 199–206 (2006)CrossRefGoogle Scholar
  2. 2.
    Apte, C., Weiss, S.: Data mining with decision trees and decision rules. Future Generation Computer Systems (1997)Google Scholar
  3. 3.
    Chang, C.C., Chen, R.S.: Using data mining technology to solve classification problems. A Case Study of Campus Digital Library, Institute of Information Management, National Chiao Tung University, Hsinchu, (2006)Google Scholar
  4. 4.
    Mai, C.K., Krishna, M., Reddy, A.V.: Poly Analyst Application for Forest Data Mining, IEEE, (2005)Google Scholar
  5. 5.
    Crone, S.F., Lessmann, S., Stahlbock, R.: The impact of pre-processing on data mining: An evaluation of classifier sensitivity in direct marketing. Eur. J. Oper. Res. 173, 781–800 (2006)CrossRefGoogle Scholar
  6. 6.
    Kirkos, E., Spathis, C., Manolopoulos, Y.: Data mining techniques for the detection of fraudulent financial statements. Expert Syst. Appl. 32, 995–1003 (2007)CrossRefGoogle Scholar
  7. 7.
    Editorial of Engineering Applications of Artificial Intelligence 19, Recent Advances in Data Mining, pp. 361–362. (2006)Google Scholar
  8. 8.
    Emekci, F., Sahin, O.D., Agrawal, D., Abbadi, El: Privacy preserving decision tree learning over multiple parties. Data Knowl. Eng. 63, 348–361 (2007)CrossRefGoogle Scholar
  9. 9.
    Gürbüz, F., Özbakir, L., Yapici, H.: Classification rule discovery for the aviation incidents resulted in fatality. Knowl. Based Syst. 22(2009), 622–632 (2009)CrossRefGoogle Scholar
  10. 10.
    Gürbüz, F., Özbakir, L., Yapici, H.: Data mining and pre-processing application on component reports of an airline company in Turkey. Expert Syst. Appl. 38(2011), 6618–6626 (2011)CrossRefGoogle Scholar
  11. 11.
    Tso, G.K.F., Yau, K.K.W.: Predicting electricity energy consumption: a comparison of regression analysis, decision tree and neural networks. www.sciencedirect.com. (2005)
  12. 12.
    Shyur, H.J.: A quantitative model for aviation safety risk assessment. Computers and Industrial Engineering (2007)Google Scholar
  13. 13.
    Hand, D., Manila, H., Smyth, P.: Principles of Data Mining. Cambridge Massachusetts, London (2001)Google Scholar
  14. 14.
    Hu, X.: DB-reduction: a data pre-processing algorithm for data mining applications. Appl. Math. Lett. 16, 889–895 (2003)CrossRefGoogle Scholar
  15. 15.
    Herbert, A.: Introduction to Data Mining and Knowledge Discovery, Two Crows Corporation, 3rd edn. (1999) Google Scholar
  16. 16.
    Bineid, M., Fielding, J.P.: Development of a civil aircraft dispatch reliability prediction methodology. Aircr. Eng. Aerosp. Technol. 75(6), 588–594 (2003)CrossRefGoogle Scholar
  17. 17.
    Aitkenhead, M.J.: A co-evolving decision tree classification method. Expert Syst. Appl. 34, 18–25 (2006)CrossRefGoogle Scholar
  18. 18.
    Nazeri, Z., Jianping, Z.: Mining aviation data to understand impacts of severe weather on airspace system performance. In: Proceedings of the International Conference on Information Technology. IEEE, (2002)Google Scholar
  19. 19.
    Dessureault, S., Sinuhaji, A., Coleman, P.: Data mining mine safety data. Mining Eng. Littleton 59(8), 64–70 (2007)Google Scholar
  20. 20.
    Hsia, T.C., Shie, A.J., Chen, L.C.: Course Planning of Extension Education to Meet Market Demand by Using Data Mining Techniques-an Example of Chinkuo Technology University in Taiwan, Taiwan, (2006)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • A. B. Arockia Christopher
    • 1
    Email author
  • S. Appavu alias Balamurugan
    • 1
  1. 1.KLN College of Information TechnologyAnna UniversitySivagangaiIndia

Personalised recommendations