Removing the assumption of conditional independence from Bayesian decision models by using artificial neural networks: some practical techniques and a case study.
The article describes how artificial neural networks with special designs can be applied to approximate a subjective Bayesian decision model without the assumption of conditional independence. New techniques are proposed to resolve some of the practical difficulties during the processes of problem structuring, knowledge elicitation, quantitative modeling, and model interpretation. A Bayesian model considering the conditional dependencies to predict a teenager's marijuana use was constructed by experts using these techniques, and compared to another conventional Bayesian model which assumed conditional independence. The new approach without the assumption of conditional independence had predictive power (r = 0.7) in the test of linearity compared to the conventional approach (r = 0.58) on a data set (n = 129). Its receiver operating characteristic curve dominated the alternative approach within the range (true positive fraction > 0.7) that we were interested in. The interpretations of the possible conditional dependencies provided by the artificial neural network after the training process were consistent with the expert's descriptions.
Wu
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YC
Department of Industrial Engineering, University of Wisconsin-Madison 53706.
Gustafson
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D000293:Adolescent; D000465:Algorithms; D003661:Decision Support Techniques; D005544:Forecasting; D006801:Humans; D008385:Marijuana Smoking; D016571:Neural Networks, Computer