Abstract
In this paper we present an empirical comparison between several paradigms coming from Statistics and Artificial Intelligence for solving a supervised classification problem. The empirically compared paradigms are Bayesian Networks, Rule Induction and Logistic Regression. The problem to tackle is the prediction of women survival diagnosed with breast cancer taking into account four predictor variables gathered at the moment of the diagnosis. The data file includes 1000 diagnosed cases at the Oncological Institute of Gipuzkoa (Basque Country). The validation of the paradigms was carried out using the 10-fold cross-validation method.
This work was supported by the Diputación Foral de Gipuzkoa, under grant OF 92/1996, by the grant UPV 140.226-EA186/96 from the University of the Basque Country, and by the grant PI 95/52 from the Gobierno Vasco — Departamento de Educación, Universidades e Investigación.
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References
Andersen, S.K., Olesen, K.G., Jensen, F.V. and Jensen, F. (1989) HUGIN — a shell for building Bayesian belief universes for expert systems. Eleventh International Joint Conference on Artificial Intelligence, vol. I, pp. 1128–1133.
Clark, P., and Niblett, T. (1989) The CN2 Induction Algorithm, Machine Learning, 3 (4), pp. 261–283.
Cooper, G.F., and Herskovits, E.A. (1992) A Bayesian method for the induction of probabilistic networks from data. Machine Learning, vol. 9, no. 4, pp. 309–347.
Friedman, N., and Goldszmidt, M. (1996) Building Classifiers using Bayesian Networks. Proceedings of AAAI-96.
Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.
Hosmer, D. W., and Lemeshow, S. (1989) Applied Logistic Regression. Wiley Series in Probability and Mathematical Statistics.
Larrañaga, P., Murga, R., Poza, M., and Kuijpers, C. (1996) Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms. Learning from Data: AI and Statistics V Lecture Notes in Statistics 112. D. Fisher, H.-J. Lenz (eds.), New York, NY: Spriger-Verlag, pp. 165–174.
Larrañaga, P., Poza, M., Yurramendi, Y., Murga, R., and Kuijpers, C. (1996) Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, pp. 912–926.
Larrañaga, P., Kuijpers, C., Murga, R., and Yurramendi, Y. (1996) Bayesian Network Structures by searching for the best ordering with genetic algorithms. IEEE Transactions on System, Man and Cybernetics. Vol 26, no. 4, pp. 487–492.
Larrañaga, P., Kuijpers, C., Poza, M., and Murga, R. (1997) Decomposing Bayesian Networks by Genetic Algorithms. Statistics and Computing. In press.
Larrañaga, P., Kuijpers, C., Murga, R., Yurramendi, Y., Graña, M., Lozano, J.A., Albizuri, X., D'Anjou, A., and Torrealdea, F.J. (1996) Genetic Algorithms applied to Bayesian Networks. A. Gammerman (ed.) Computational Learning and Probabilistic Reasoning. John Wiley, pp. 211–234.
Lauritzen, S.L., and Spiegelhalter, D.J. (1988) Local computations with probabilities on graphical structures and their application on expert systems. J.R. Statist. Soc. B, vol. 50, no. 2, pp. 157–224.
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© 1997 Springer-Verlag Berlin Heidelberg
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Larrañaga, P., Gallego, M.J., Sierra, B., Urkola, L., Michelena, M.J. (1997). Bayesian networks, rule induction and logistic regression in the prediction of women survival suffering from breast cancer. In: Coasta, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 1997. Lecture Notes in Computer Science, vol 1323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0023932
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DOI: https://doi.org/10.1007/BFb0023932
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