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Reseach Article

Training Set Size for Generalization Ability of Artificial Neural Networks in Forecasting TCP/IP Traffic Trends

by Vusumuzi Moyo, Khulumani Sibanda
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 13
Year of Publication: 2015
Authors: Vusumuzi Moyo, Khulumani Sibanda
10.5120/19885-1902

Vusumuzi Moyo, Khulumani Sibanda . Training Set Size for Generalization Ability of Artificial Neural Networks in Forecasting TCP/IP Traffic Trends. International Journal of Computer Applications. 113, 13 ( March 2015), 14-19. DOI=10.5120/19885-1902

@article{ 10.5120/19885-1902,
author = { Vusumuzi Moyo, Khulumani Sibanda },
title = { Training Set Size for Generalization Ability of Artificial Neural Networks in Forecasting TCP/IP Traffic Trends },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 13 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number13/19885-1902/ },
doi = { 10.5120/19885-1902 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:50.150432+05:30
%A Vusumuzi Moyo
%A Khulumani Sibanda
%T Training Set Size for Generalization Ability of Artificial Neural Networks in Forecasting TCP/IP Traffic Trends
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 13
%P 14-19
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we empirically investigate various sizes of training sets with the aim of determining the optimum training set size for generalization ability of an ANN trained on forecasting TCP/IP network traffic trends. We found from both the simulation experiments and literature that the best training set size can be obtained by selecting training samples randomly, between the interval 5×N_W and 10×N_W in number, depending on the difficulty of the problem under consideration.

References
  1. S. Chabaa, "Identification and Prediction of Internet Traffic Using Artificial Neural Networks," J. Intell. Learn. Syst. Appl. , vol. 02, no. 03, pp. 147–155, 2010.
  2. R. Aamodt, "Using Artificial Neural Networks To Forecast Financial Time Series," Norwegian university of science and technology, 2010.
  3. H. Tong, C. Li, J. He, and Y. Chen, "Internet Traffic Prediction by W-Boost: Classification and Regression," Neural Comput. , vol. 2, no. 973, pp. 397–402, 2005.
  4. E. Richards, "Generalization in Neural Networks, Experiments in Speech Recognition," University of Colarado, 1991.
  5. H. Leung and W. Zue, "On the Generalization Capability of Multi-Layered Networks in the Extraction of Speech Properties," in International Conference on Acoustics, Speech and Signal Processing. 1989, pp. 422–425.
  6. A. Weigend, D. Rumelhart and B. Huberman, "Predicting the future: a connectionist approach. " Int. J. Neural Syst. , vol. 1, no. 3, pp. 193–209, 1990.
  7. S. Haykin, Neural Networks: A comprehensive foundation, Second. Pearson, 1999, pp. 2–3.
  8. T. Mitchell, Machine learning. McGraw Hill Publishers, 1997, pp. 100–150.
  9. E. Sontag, "Feedback stabilization using two hidden layer nets," IEEE Trans. Neural Networks, vol. 3, no. 6, pp. 34–60, 1992
  10. H. R. Maier and G. C. Dandy, "Determining Inputs for Neural Network Models of Multivariate Time Series," Comput. Civ. Infrastruct. Eng. , vol. 12, no. 5, pp. 353–368, Sep. 1997.
  11. T. Kavzoglu, "Determining Optimum Structure for Artificial Neural Networks," In proceedings of the 25th Annual Technical Conference and Exhibition of the Remote Sensing Society, 1999, no. September, pp. 675–682.
  12. G. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art," Int. J. Forecast. , vol. 14, no. July, pp. 35–62, 1998
  13. N. Lange, C. M. Bishop, and B. D. Ripley, "Neural Networks for Pattern Recognition. ," J. Am. Stat. Assoc. , vol. 92, no. 440, p. 1642, Dec. 1997
  14. E. Baum and D. Haussler, "What size net gives valid generalization," Neural Comput. , vol. 1, no. 1, pp. 159–161, 1989.
  15. D. Hush, "Classification with neural networks," in Proceedings of the IEEE International Conference on Systems Engineering, 1989, pp. 50–57.
  16. C. Klimasauskas, Applying neural networks. 1993, pp. 47–72.
Index Terms

Computer Science
Information Sciences

Keywords

Generalization ability Artificial Neural Networks and Training set size.