Automation of generalized additive neural networks for predictive data mining
De Waal, David Andre
Du Toit, Jan Valentine
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For a new technology to make the step from experimental technology to mainstream technology, tools need to be created to facilitate the use of the developed technology in the envisaged application area. Generalized additive neural networks provide an attractive framework that shows promise in the field of predictive data mining. However, the construction of such networks is very time consuming and subjective, because it depends on the user to interpret partial residual plots and to make changes in the neural network architecture. For this technology to be accepted as a serious modeling option in the field of predictive data mining the construction process needs to be automated and the benefits of using the technique must be clearly illuminated. This article shows how intelligent search may be used to replace subjective human judgment with objective criteria and make generalized additive neural networks an attractive option for the modeler.