Learning Bayesian Networks by Genetic Algorithms. A case study in the prediction of survival in malignant skin melanoma (1997)
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BibTeX
@MISC{Larranaga97learningbayesian,
author = {P. Larranaga and B. Sierra and M. J. Gallego and M. J. Michelena and J. M. Picaza},
title = {Learning Bayesian Networks by Genetic Algorithms. A case study in the prediction of survival in malignant skin melanoma},
year = {1997}
}
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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 called Naive-Bayes. In both cases, the estimation of the model accuracy is obtained from the 10-fold cross-validation method. 1. Introduction Expert systems, one of the most developed areas in the field of Artificial Intelligence, are computer programs designed to help or replace humans beings in tasks in which the human experience and human knowledge are scarce and unreliable. Although, there are domains in which the tasks can be specifed by logic rules, other domains are characterized by an uncertainty inherent...
