PREDICTING SURVIVAL IN MALIGNANT SKILL MELANOMA USING BAYESIAN NETWORKS AUTOMATICALLY INDUCED BY GENETIC ALGORITHMS - AN EMPIRICAL-COMPARISON BETWEEN DIFFERENT APPROACHES
B. Sierra et P. Larranaga, PREDICTING SURVIVAL IN MALIGNANT SKILL MELANOMA USING BAYESIAN NETWORKS AUTOMATICALLY INDUCED BY GENETIC ALGORITHMS - AN EMPIRICAL-COMPARISON BETWEEN DIFFERENT APPROACHES, Artificial intelligence in medicine, 14(1-2), 1998, pp. 215-230
In this work we introduce a methodology based on genetic algorithms fo
r the automatic induction of Bayesian networks from a file containing
cases and variables related to the problem. The structure is learned b
y applying three different methods: The Cooper and Herskovits metric f
or a general Bayesian network, the Markov blanket approach and the rel
axed Markov blanket method. The methodologies are applied to the probl
em of predicting survival of people after 1, 3 and 5 years of being di
agnosed as having malignant skin melanoma. The accuracy of the obtaine
d models, measured in terms of the percentage of well-classified subje
cts, is compared to that obtained by the so-called Naive-Bayes. In the
four approaches, the estimation of the model accuracy is obtained fro
m the 10-fold cross-validation method. (C) 1998 Elsevier Science B.V.
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