Abstract
Most real-world applications of diagnosis involve continuous-valued attributes, which are normally discretized before the existing classification algorithms are applied. The discretization may be based on data or on human expertise. In cellular networks the number of classified examples is very limited. Thus, the diagnosis experts should specify the boundaries of the intervals for each discretized symptom. The large number of values makes it difficult to specify precise parameters. Even if boundaries are obtained from classified examples, due to the limited number of cases, the obtained values are not very accurate. In this paper two techniques to improve the performance of diagnosis systems based on Bayesian Networks are compared. Some empirical results are presented for diagnosis in a GSM network. The first method, Smooth Bayesian Networks, is shown to be more robust to imprecise setting of boundaries. The second method, Multiple Uniform Intervals, is superior if accurately defined boundaries are available.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)
Dougherty, K., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proc.12th Intern. Conference on Machine Learning, Tahoe City, CA, pp. 194–202. Morgan Kaufmann, San Mateo (1995)
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous valued attributes for classification learning. In: Proc. 13th Intern. Joint Conf. on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann, San Francisco (1993)
Yang, Y., Webb, G.I.: A Comparative Study of Discretization Methods for Naive-Bayes Classifiers. In: Proceddings of PKAW 2002, Tokyo, Japan, pp. 159–173 (2002)
Barco, R., Díez, L., Wille, V.: Smoothing boundaries of states in Bayesian Networks: Application to diagnosis in cellular systems. Submitted to Artificial Intelligence. Elsevier, Amsterdam (February 2005)
Laiho, J., Kylvj, M., Hglund, A.: Utilisation of Advanced Analysis Methods in UMTS Networks. In: Proc. IEEE 55th Vehicular Techn. Conf., pp. 726–730 (2002)
Jakobson, G., Weissman, M.D.: Alarm Correlation. IEEE Network 7(6), 52–59 (1993)
Barco, R., Wille, V., Díez, L.: System for automatic diagnosis in a cellular network based on performance indicators. European Transactions on Telecommunications 15 (2005)
Domingos, P., Pazzani, P.: On the optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning 29, 103–130 (1997)
Barco, R., et al.: Automated troubleshooting of mobile networks using Bayesian Networks. In: Proc. IASTED Intern. Conf. on Communication Systems and Networks, pp. 105–110. Acta Press (2002)
Barco, R., et al.: Automated troubleshooting of a mobile communication network using Bayesian networks. In: Proc. 4th IEEE Intern.Workshop on Mobile and Wireless Communications Network, pp. 606–610 (2002)
Pan, H., McMichael, D.: Fuzzy causal probabilistic networks - a new ideal and practical inference engine. In: Proc. 1st International Conference on Multisource-Multisensor Information Fusion (1998)
Pan, H.: Fuzzy Bayesian Networks - A General Formalism for Representation, Inference and Learning with Hybrid Bayesian Networks. International Journal of Pattern Recognition and Artificial Intelligence 14(7), 941–963 (2000)
Yang, C.C.: Fuzzy Bayesian Inference. In: Proc. IEEE International Conference on Systems, Man and Cybernetics, Orlando, FL, pp. 2707–2712 (1997)
Valtorta, M., Vomlel, J.: Soft Evidential Update for Probabilistic Multiagent Systems. International Journal of Approximate Reasoning 29(1), 71–106 (2002)
Pradhan, M., et al.: The sensitivity of belief networks to imprecise probabilities: An experimental investigation. Artificial Intelligence 85(1-2), 363–397 (1996)
Kipersztok, O., Wang, H.: Another Look at Sensitivity of Bayesian Networks to Imprecise Probabilities. In: Proc. 8th International Workshop on Artificial Intelligence and Statistics (2001)
Henrion, M., et al.: Why is diagnosis using belief networks insensitive to imprecision in probabilities? In: Proc.12th Conf. on Uncertainty in Artificial Intelligence, pp. 307–314. Morgan Kaufmann, San Francisco (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barco, R., Lázaro, P., Díez, L., Wille, V. (2005). Multiple Intervals Versus Smoothing of Boundaries in the Discretization of Performance Indicators Used for Diagnosis in Cellular Networks. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_100
Download citation
DOI: https://doi.org/10.1007/11424925_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25863-6
Online ISBN: 978-3-540-32309-9
eBook Packages: Computer ScienceComputer Science (R0)