Optimising performance indicators in the telecommunications sector
DOI:
https://doi.org/10.4108/eetismla.8717Keywords:
performance in the telecommunication sector, neural network on performance indicators, supervised learning, multilayer neural network, public telecommunication sectorAbstract
INTRODUCTION: This study analyses and predicts performance in the public telecommunication sector using neural networks on key performance indicators in the telecommunication sector.
OBJECTIVES: Although there are several key performance indicators in the telecommunications sector, we have selected a few and assessed their correlation with the variable to be predicted. In this study, we used supervised learning based on a multi-layer neural network.
METHODS: The algorithm used in this study is the retro propagation algorithm because of its simplicity and accuracy of estimation.
RESULTS: The results show that the selected indicators, including accessibility, maintainability, satisfaction, network operating cost and availability, explain more than 80% of the performance in the telecommunications sector, and the area of the ROC curve is equal to 0.97, which means that the classifier is almost perfect. This is also justified by the sensitivity and specificity, which are close to 1 when observing the ROC curve and the confusion matrix. The classification error found from the confusion matrix is equal to 1%, which means that our model has very high accuracy.
CONCLUSION: The other indicators presented were not selected in the model because of their low correlation with the variable of interest and the difficulty of collecting the data.
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References
[1] Alpaydin, E. (2014). Introduction to machine learning. MIT press.
[2] Alpaydin, E. (2020). Introduction to machine learning. MIT Press.
[3] "Applied Logistic Regression" by Hosmer Jr, Lemeshow, and Sturdivant, published in 2013.
[4] Babbie, E. (2016). The practice of social research. Cengage Learning.
[5] Cortes, C., Pregibon, D. Signature-based methods for data streams. Data Mining and Knowledge Discovery 2001; 5(3):167-182. DOI: https://doi.org/10.1023/A:1011464915332
[6] Cortes, C., Pregibon, D. Giga-mining. Proceedings of the Fourth International Conference on Knowledge Department of Telecommunications. 1992. Indian Telecommunication Statistics 1991.
[7] Discovery and Data Mining; 174-178, 1998 August 27-31; New York, NY: AAAI Press, 1998.
[8] EUROSTAT. 1994. Telecommunication Indicators. Documento distribuido en el ICOBS/AS Working.
[9] Ezawa, K., Norton, S. Knowledge discovery in telecommunication services data using Bayesian network models. Proceedings of the First International Conference on Knowledge Discovery and Data Mining; 1995 August 20-21. Montreal Canada. AAAI Press: Menlo Park, CA, 1995.
[10] Fawcett, T., Provost, F. Adaptive fraud detection. Data Mining and Knowledge Discovery 1997; 1(3):291-316. DOI: https://doi.org/10.1023/A:1009700419189
[11] Fawcett, T, Provost, F. Activity monitoring: Noticing interesting changes in behavior. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 53-62. San Diego. ACM Press: New York, NY, 1999. DOI: https://doi.org/10.1145/312129.312195
[12] Group Meeting, 12-13 de enero de 1994, Luxembourg. Federal Communications Commission. 1992.
[13] Han, J., Altman, R. B., Kumar, V., Mannila, H., Pregibon, D. Emerging scientific applications in data mining. Communications of the ACM 2002; 45(8): 54-58. DOI: https://doi.org/10.1145/545151.545179
[14] Kaplan, H., Strauss, M., Szegedy, M. Just the fax-differentiating voice and fax phone lines using call billing data. Proceedings of the Tenth Annual ACM-SIAM Symposium on Data Mining In Telecommunications.
[15] Discrete Algorithms. 935-936. Baltimore, Maryland. Society for Industrial and Applied Mathematics: Philadelphia, PA, 1999.
[16] Klemettinen, M., Mannila, H., Toivonen, H. Rule discovery in telecommunication alarm data. Journal of Network and Systems Management 1999; 7(4):395-423. DOI: https://doi.org/10.1023/A:1018787815779
[17] Mani, D. R., Drew, J., Betz, A., Datta, P. Statistics and data mining techniques for lifetime value modeling. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 94-103. San Diego. ACM Press: New York, NY, 1999. DOI: https://doi.org/10.1145/312129.312205
[18] Roset, S., Murad, U., Neumann, E., Idan, Y., Pinkas, G. Discovery of fraud rules for telecommunications-challenges and solutions. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 409-413, San Diego CA. New York: ACM Press, 1999. DOI: https://doi.org/10.1145/312129.312303
[19] Sasisekharan, R., Seshadri, V., Weiss, S. Data mining and forecasting in large-scale telecommunication networks. IEEE Expert 1996; 11(1):37-43. DOI: https://doi.org/10.1109/64.482956
[20] Weiss, G. M., Hirsh, H. Learning to predict rare events in event sequences. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. 359-363. AAAI Press, 1998.
[21] Weiss, G. M., Provost, F. Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research 2003; 19:315- 354. DOI: https://doi.org/10.1613/jair.1199
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