Skip to main content
SpringerLink
Log in

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

  • Conference paper
Intelligent Computing, Networking, and Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chang, A.S., Leu, S.S.: Data mining model for identifying project profitability variables. Int. J. Project Manage. 24, 199–206 (2006)

    Article  Google Scholar 

  2. Apte, C., Weiss, S.: Data mining with decision trees and decision rules. Future Generation Computer Systems (1997)

    Google Scholar 

  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. Mai, C.K., Krishna, M., Reddy, A.V.: Poly Analyst Application for Forest Data Mining, IEEE, (2005)

    Google Scholar 

  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)

    Article  Google Scholar 

  6. Kirkos, E., Spathis, C., Manolopoulos, Y.: Data mining techniques for the detection of fraudulent financial statements. Expert Syst. Appl. 32, 995–1003 (2007)

    Article  Google Scholar 

  7. Editorial of Engineering Applications of Artificial Intelligence 19, Recent Advances in Data Mining, pp. 361–362. (2006)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Shyur, H.J.: A quantitative model for aviation safety risk assessment. Computers and Industrial Engineering (2007)

    Google Scholar 

  13. Hand, D., Manila, H., Smyth, P.: Principles of Data Mining. Cambridge Massachusetts, London (2001)

    Google Scholar 

  14. Hu, X.: DB-reduction: a data pre-processing algorithm for data mining applications. Appl. Math. Lett. 16, 889–895 (2003)

    Article  Google Scholar 

  15. Herbert, A.: Introduction to Data Mining and Knowledge Discovery, Two Crows Corporation, 3rd edn. (1999)

    Google Scholar 

  16. Bineid, M., Fielding, J.P.: Development of a civil aircraft dispatch reliability prediction methodology. Aircr. Eng. Aerosp. Technol. 75(6), 588–594 (2003)

    Article  Google Scholar 

  17. Aitkenhead, M.J.: A co-evolving decision tree classification method. Expert Syst. Appl. 34, 18–25 (2006)

    Article  Google Scholar 

  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. Dessureault, S., Sinuhaji, A., Coleman, P.: Data mining mine safety data. Mining Eng. Littleton 59(8), 64–70 (2007)

    Google Scholar 

  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 

Download references

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.

Author information

Authors and Affiliations

  1. KLN College of Information Technology, Anna University, Sivagangai, Tamilnadu, India

    A. B. Arockia Christopher & S. Appavu alias Balamurugan

Authors
  1. A. B. Arockia Christopher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Appavu alias Balamurugan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. B. Arockia Christopher .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Orissa, India

    Durga Prasad Mohapatra

  2. Computer Science and Engineering, SOA University, Bhubaneswar, India

    Srikanta Patnaik

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer India

About this paper

Cite this paper

Arockia Christopher, A.B., Appavu alias Balamurugan, S. (2014). Prediction of Warning Level in Aircraft Accidents using Classification Techniques: An Empirical Study. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_126

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-1665-0_126

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1664-3

  • Online ISBN: 978-81-322-1665-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation