Abstract
In this work we introduce a methodology based on Genetic Algorithms for the automatic induction of Bayesian Networks from a file containing cases and variables related to the problem. The methodology is applied to the problem of predicting survival of people after one, three and five years of being diagnosed as having malignant skin melanoma. The accuracy of the obtained model, measured in terms of the percentage of well-classified subjects, is compared to that obtained by the so-called Naive-Bayes. In both cases, the estimation of the model accuracy is obtained from the 10-fold cross-validation method.
We thank Gregory F. Cooper for providing his simulation of the ALARM Network. We also thank the referees for their work and comments. 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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Larrañaga, P., Sierra, B., Gallego, M.J., Michelena, M.J., Picaza, J.M. (1997). Learning Bayesian Networks by Genetic Algorithms: A case study in the prediction of survival in malignant skin melanoma. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029459
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DOI: https://doi.org/10.1007/BFb0029459
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