Predicting Network Faults using Random Forest and C5.0

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The Internet is an enabling technology that assists daily and business activities. However, a network fault could prevent the user from accessing the internet thus creating trouble tickets. Ideally, accurate prediction prior to network fault allows the telco to respond before the customer raises a trouble ticket. Current research focuses on forecasting the quantity of trouble ticket using historical trouble ticket. To improve the prediction of network fault, the customer trouble ticket data is augmented to include internet usage data and signal measurement data. Random Forest (RF) and C5.0 Decision Tree algorithms are used to derive predictive models. Experiment results reveal that RF shows higher AUC score as compared to C5.0 Decision Tree. RF is able to identify the important features while C5.0 Decision Tree is able to list decision rules that describe the relation among selected features.

     

     


  • Keywords


    Broadband Network; C5.0 Decision Tree; Network Fault Prediction; Random Forest; Telecommunication

  • References


      [1] Sonny Yuhensky, Rendy Munadi, et al. Forecasting formulation model for amount of fault of the cpe segment on broadband network pt. telkom using arima method. In Control, Electronics, Renewable Energy and Communications (ICCEREC), 2016 International Conference on, pages 185–191. IEEE, 2016.

      [2] Zˇeljko Deljac, Mirko Randic ́, and Gordan Krcˇelic ́. A multivariate approach to predicting quantity of failures in broadband networks based on a recurrent neural network. Journal of network and systems management, 24(1):189–221, 2016.

      [3] Angelos K Marnerides, Simon Malinowski, Ricardo Morla, and Hyong S Kim. Fault diagnosis in dsl networks using support vector machines. Computer Communications, 62:72–84, 2015.

      [4] Lam Hai Shuan, Tan Yi Fei, Soo Wooi King, Guo Xiaoning, and Lee Zhe Mein. Network equipment failure prediction with big data analytics. International Journal of Advances in Soft Computing & Its Applications, 8(3), 2016.

      [5] Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Philip, S.Y. & Zhou, Z.H (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1), 1-37.

      [6] Du, M., Wang, S. M., & Gong, G. (2011). Research on decision tree algorithm based on information entropy. In Advanced Materials Research (Vol. 267, pp. 732-737). Trans Tech Publications.

      [7] Yang, B. S., Di, X., & Han, T. (2008). Random forests classifier for machine fault diagnosis. Journal of mechanical science and technology, 22(9), 1716-1725.

      [8] Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

      [9] Huang, J., & Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on knowledge and Data Engineering, 17(3), 299-310.


 

View

Download

Article ID: 11164
 
DOI: 10.14419/ijet.v7i2.14.11164




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.